SlideShare uma empresa Scribd logo
1 de 80
SHOGUN

 2011 4   23       9      CV       PRML
               @yasutomo57jp (   @inco_san   )
SHOGUN
   1                       SHOGUN

 2011 4   23       9      CV       PRML
               @yasutomo57jp (   @inco_san   )
SHOGUN
*
OpenCV


 * http://d.hatena.ne.jp/takmin/20110306/1299423617
• SHOGUN
• SHOGUN
• 1        SHOGUN
• SHOGUN
• 1                     SHOGUN
 •   Static Interface
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
• SHOGUN
• 1                          SHOGUN
    •   Static Interface


•       2           SHOGUN
    •   Modular Interface


•       3           C++           (   )
    •   libshogun
SHOGUN
SHOGUN
•
SHOGUN
•
    •   SVM   !
SHOGUN
•
    •           SVM                    !

        • SVM         OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT
SHOGUN
•
    •           SVM                       !

        • SVM          OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT



        •             Linear, Polynomial, Gaussian and Sigmoid Kernel
SHOGUN
•
    •           SVM                       !

        • SVM          OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT



        •             Linear, Polynomial, Gaussian and Sigmoid Kernel



    •
SHOGUN
• SVM             !!

 • LDA : Linear Discriminant Analysis
 • LPM : Linear Programming Machine
 • (Kernel) Perceptron
 • HMM
SHOGUN


•
•
SHOGUN
Q. Matlab
Q. Matlab
Octave
Python
Python
Q. C++
         …
…
…
SHOGUN
SHOGUN
• Static Interface
    •
    •
•    Modular Interface
    • Python Octave
    •
•    libshogun
    •   C++
    •
• Static Interface
    •
    •
•    Modular Interface
    • Python Octave
    •
•    libshogun
    •   C++
    •
Windows
Cygwin

http://www.shogun-toolbox.org/#releases
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases



          Mac
sudo port install shogun
Windows
                                           Linux (Ubuntu)
Cygwin
                                          sudo apt-get install shogun
http://www.shogun-toolbox.org/#releases



          Mac
sudo port install shogun                                       OK
SVM
        ••      libsvm
                                                    (Cmdline   )


set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                libsvm
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                libsvm
c1                                                    C      1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM
        ••      libsvm
                                                                   (Cmdline             )


set_kernel GAUSSIAN REAL 10 1.2                                                  (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
••      libsvm
                                                                   (Cmdline             )


set_kernel SIGMOID REAL 50 3 0                                                   (cache, gamma, coeff)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIBSVM                                      libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
SVM

        ••      svmlight
                                                    (Cmdline   )


set_kernel GAUSSIAN REAL 10 1.2
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                    (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                            (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                 libsvm
c1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                             (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                     (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                 libsvm
c1                                                    C      1

train_classifier
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline      )


set_kernel GAUSSIAN REAL 10 1.2                                           (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify
SVM

        ••      svmlight
                                                                   (Cmdline             )


set_kernel GAUSSIAN REAL 10 1.2                                                  (cache, kernel width)
set_features TRAIN ../data/fm_train_real.dat
set_labels TRAIN ../data/label_train_twoclass.dat
new_classifier LIGHT                                       libsvm
c1                                                          C      1

train_classifier                                     SVM
save_classifier libsvm.model

load_classifier libsvm.model LIBSVM
set_features TEST ../data/fm_test_real.dat
out.txt=classify                                                       out.txt
Python

• sg            ( from sg import sg                )

 • sg                                         OK

   • Cmdline       set_feature TEST data.dat

   • Python        sg(‘set_feature’, ‘TEST’, ‘data.dat’)


     http://www.shogun-toolbox.org/doc/static_tutorial.html
• SHOGUN
• SHOGUN
•3
• SHOGUN
•3
 • Static Interface,Modular Interface, libshogun
• SHOGUN
•3
 • Static Interface,Modular Interface, libshogun
 •          Static Interface
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
• SHOGUN
•3
  • Static Interface,Modular Interface, libshogun
  •          Static Interface

•
           Modular Interface

Mais conteúdo relacionado

Mais procurados

Programming using Open Mp
Programming using Open MpProgramming using Open Mp
Programming using Open MpAnshul Sharma
 
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011bobmcwhirter
 
Ffmpeg
FfmpegFfmpeg
Ffmpegduquoi
 
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2Amar Kapadia
 
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java ApplicationFind bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Applicationguest1f2740
 
DataMapper on Infinispan
DataMapper on InfinispanDataMapper on Infinispan
DataMapper on InfinispanLance Ball
 
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBoxComplex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBoxbobmcwhirter
 
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011Lance Ball
 
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121ZK_Arch_notes_20081121
ZK_Arch_notes_20081121WANGCHOU LU
 
20140419 oedo rubykaigi04
20140419 oedo rubykaigi0420140419 oedo rubykaigi04
20140419 oedo rubykaigi04Hiroshi SHIBATA
 
Configuration management II - Terraform
Configuration management II - TerraformConfiguration management II - Terraform
Configuration management II - TerraformXavier Serrat Bordas
 
How to distribute Ruby to the world
How to distribute Ruby to the worldHow to distribute Ruby to the world
How to distribute Ruby to the worldHiroshi SHIBATA
 
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan201420140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014Hiroshi SHIBATA
 

Mais procurados (20)

Programming using Open Mp
Programming using Open MpProgramming using Open Mp
Programming using Open Mp
 
TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011TorqueBox at DC:JBUG - November 2011
TorqueBox at DC:JBUG - November 2011
 
Ffmpeg
FfmpegFfmpeg
Ffmpeg
 
OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2OpenStack Swift Command Line Reference Diablo v1.2
OpenStack Swift Command Line Reference Diablo v1.2
 
Find bottleneck and tuning in Java Application
Find bottleneck and tuning in Java ApplicationFind bottleneck and tuning in Java Application
Find bottleneck and tuning in Java Application
 
DataMapper on Infinispan
DataMapper on InfinispanDataMapper on Infinispan
DataMapper on Infinispan
 
RubyGems 3 & 4
RubyGems 3 & 4RubyGems 3 & 4
RubyGems 3 & 4
 
Complex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBoxComplex Made Simple: Sleep Better with TorqueBox
Complex Made Simple: Sleep Better with TorqueBox
 
Tips of Malloc & Free
Tips of Malloc & FreeTips of Malloc & Free
Tips of Malloc & Free
 
TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011TorqueBox - Ruby Hoedown 2011
TorqueBox - Ruby Hoedown 2011
 
ZK_Arch_notes_20081121
ZK_Arch_notes_20081121ZK_Arch_notes_20081121
ZK_Arch_notes_20081121
 
Ruby 2.4 Internals
Ruby 2.4 InternalsRuby 2.4 Internals
Ruby 2.4 Internals
 
20140419 oedo rubykaigi04
20140419 oedo rubykaigi0420140419 oedo rubykaigi04
20140419 oedo rubykaigi04
 
Lec7
Lec7Lec7
Lec7
 
Configuration management II - Terraform
Configuration management II - TerraformConfiguration management II - Terraform
Configuration management II - Terraform
 
How to distribute Ruby to the world
How to distribute Ruby to the worldHow to distribute Ruby to the world
How to distribute Ruby to the world
 
20140925 rails pacific
20140925 rails pacific20140925 rails pacific
20140925 rails pacific
 
RubyGems 3 & 4
RubyGems 3 & 4RubyGems 3 & 4
RubyGems 3 & 4
 
20140425 ruby conftaiwan2014
20140425 ruby conftaiwan201420140425 ruby conftaiwan2014
20140425 ruby conftaiwan2014
 
JVM Internals (2015)
JVM Internals (2015)JVM Internals (2015)
JVM Internals (2015)
 

Semelhante a SHOGUN使ってみました

Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance Tunningguest1f2740
 
Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance TunningTerry Cho
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machinesbutest
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machinesbutest
 
Beirut Java User Group JVM presentation
Beirut Java User Group JVM presentationBeirut Java User Group JVM presentation
Beirut Java User Group JVM presentationMahmoud Anouti
 
Новый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоныНовый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоныTimur Safin
 
Software Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in JavaSoftware Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in JavaIsuru Perera
 
Introduction to LAVA Workload Scheduler
Introduction to LAVA Workload SchedulerIntroduction to LAVA Workload Scheduler
Introduction to LAVA Workload SchedulerNopparat Nopkuat
 
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108Mathias Herberts
 
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X SandboxMeder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X SandboxDefconRussia
 
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM StackGrow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM StackKeitaSugiyama1
 
Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018Petr Zapletal
 
Python + GDB = Javaデバッガ
Python + GDB = JavaデバッガPython + GDB = Javaデバッガ
Python + GDB = JavaデバッガKenji Kazumura
 
DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)Soshi Nemoto
 
Adding a BOLT pass
Adding a BOLT passAdding a BOLT pass
Adding a BOLT passAmir42407
 
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container DayQuantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container DayPhil Estes
 

Semelhante a SHOGUN使ってみました (20)

Jvm internals
Jvm internalsJvm internals
Jvm internals
 
Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance Tunning
 
Jvm Performance Tunning
Jvm Performance TunningJvm Performance Tunning
Jvm Performance Tunning
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
 
Tutorial - Support vector machines
Tutorial - Support vector machinesTutorial - Support vector machines
Tutorial - Support vector machines
 
Beirut Java User Group JVM presentation
Beirut Java User Group JVM presentationBeirut Java User Group JVM presentation
Beirut Java User Group JVM presentation
 
Новый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоныНовый InterSystems: open-source, митапы, хакатоны
Новый InterSystems: open-source, митапы, хакатоны
 
Software Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in JavaSoftware Profiling: Understanding Java Performance and how to profile in Java
Software Profiling: Understanding Java Performance and how to profile in Java
 
Introduction to LAVA Workload Scheduler
Introduction to LAVA Workload SchedulerIntroduction to LAVA Workload Scheduler
Introduction to LAVA Workload Scheduler
 
New features in Ruby 2.5
New features in Ruby 2.5New features in Ruby 2.5
New features in Ruby 2.5
 
Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108Artimon - Apache Flume (incubating) NYC Meetup 20111108
Artimon - Apache Flume (incubating) NYC Meetup 20111108
 
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
Deep Learning for Computer Vision: Software Frameworks (UPC 2016)
 
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X SandboxMeder Kydyraliev - Mining Mach Services within OS X Sandbox
Meder Kydyraliev - Mining Mach Services within OS X Sandbox
 
Grow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM StackGrow and Shrink - Dynamically Extending the Ruby VM Stack
Grow and Shrink - Dynamically Extending the Ruby VM Stack
 
Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018Adopting GraalVM - Scale by the Bay 2018
Adopting GraalVM - Scale by the Bay 2018
 
Python + GDB = Javaデバッガ
Python + GDB = JavaデバッガPython + GDB = Javaデバッガ
Python + GDB = Javaデバッガ
 
DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)DevOps(4) : Ansible(2) - (MOSG)
DevOps(4) : Ansible(2) - (MOSG)
 
[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe[BGOUG] Java GC - Friend or Foe
[BGOUG] Java GC - Friend or Foe
 
Adding a BOLT pass
Adding a BOLT passAdding a BOLT pass
Adding a BOLT pass
 
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container DayQuantifying Container Runtime Performance: OSCON 2017 Open Container Day
Quantifying Container Runtime Performance: OSCON 2017 Open Container Day
 

Mais de Yasutomo Kawanishi

TransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by TransformerTransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by TransformerYasutomo Kawanishi
 
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...Yasutomo Kawanishi
 
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~Yasutomo Kawanishi
 
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~Yasutomo Kawanishi
 
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜Yasutomo Kawanishi
 
サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識Yasutomo Kawanishi
 
Pythonによる画像処理について
Pythonによる画像処理についてPythonによる画像処理について
Pythonによる画像処理についてYasutomo Kawanishi
 
背景モデリングに関する研究など
背景モデリングに関する研究など背景モデリングに関する研究など
背景モデリングに関する研究などYasutomo Kawanishi
 
画像処理でのPythonの利用
画像処理でのPythonの利用画像処理でのPythonの利用
画像処理でのPythonの利用Yasutomo Kawanishi
 
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節Yasutomo Kawanishi
 
SNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシングSNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシングYasutomo Kawanishi
 

Mais de Yasutomo Kawanishi (12)

TransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by TransformerTransPose: Towards Explainable Human Pose Estimation by Transformer
TransPose: Towards Explainable Human Pose Estimation by Transformer
 
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
全日本コンピュータビジョン勉強会:Disentangling and Unifying Graph Convolutions for Skeleton-B...
 
Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~Pythonによる機械学習入門 ~Deep Learningに挑戦~
Pythonによる機械学習入門 ~Deep Learningに挑戦~
 
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~Pythonによる機械学習入門 ~SVMからDeep Learningまで~
Pythonによる機械学習入門 ~SVMからDeep Learningまで~
 
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜Pythonによる機械学習入門〜基礎からDeep Learningまで〜
Pythonによる機械学習入門〜基礎からDeep Learningまで〜
 
サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識サーベイ論文:画像からの歩行者属性認識
サーベイ論文:画像からの歩行者属性認識
 
Pythonによる画像処理について
Pythonによる画像処理についてPythonによる画像処理について
Pythonによる画像処理について
 
ACCV2014参加報告
ACCV2014参加報告ACCV2014参加報告
ACCV2014参加報告
 
背景モデリングに関する研究など
背景モデリングに関する研究など背景モデリングに関する研究など
背景モデリングに関する研究など
 
画像処理でのPythonの利用
画像処理でのPythonの利用画像処理でのPythonの利用
画像処理でのPythonの利用
 
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節第17回関西CVPRML勉強会 (一般物体認識) 1,2節
第17回関西CVPRML勉強会 (一般物体認識) 1,2節
 
SNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシングSNSでひろがるプライバシ制御センシング
SNSでひろがるプライバシ制御センシング
 

Último

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptxHampshireHUG
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slidevu2urc
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsEnterprise Knowledge
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsJoaquim Jorge
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsMaria Levchenko
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoffsammart93
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfEnterprise Knowledge
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerThousandEyes
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonAnna Loughnan Colquhoun
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreternaman860154
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUK Journal
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)wesley chun
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024The Digital Insurer
 

Último (20)

Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
04-2024-HHUG-Sales-and-Marketing-Alignment.pptx
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
Histor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slideHistor y of HAM Radio presentation slide
Histor y of HAM Radio presentation slide
 
IAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI SolutionsIAC 2024 - IA Fast Track to Search Focused AI Solutions
IAC 2024 - IA Fast Track to Search Focused AI Solutions
 
Artificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and MythsArtificial Intelligence: Facts and Myths
Artificial Intelligence: Facts and Myths
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Handwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed textsHandwritten Text Recognition for manuscripts and early printed texts
Handwritten Text Recognition for manuscripts and early printed texts
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationFrom Event to Action: Accelerate Your Decision Making with Real-Time Automation
From Event to Action: Accelerate Your Decision Making with Real-Time Automation
 
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot TakeoffStrategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
Strategize a Smooth Tenant-to-tenant Migration and Copilot Takeoff
 
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdfThe Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
The Role of Taxonomy and Ontology in Semantic Layers - Heather Hedden.pdf
 
How to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected WorkerHow to Troubleshoot Apps for the Modern Connected Worker
How to Troubleshoot Apps for the Modern Connected Worker
 
Data Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt RobisonData Cloud, More than a CDP by Matt Robison
Data Cloud, More than a CDP by Matt Robison
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 
Presentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreterPresentation on how to chat with PDF using ChatGPT code interpreter
Presentation on how to chat with PDF using ChatGPT code interpreter
 
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdfUnderstanding Discord NSFW Servers A Guide for Responsible Users.pdf
Understanding Discord NSFW Servers A Guide for Responsible Users.pdf
 
Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)Powerful Google developer tools for immediate impact! (2023-24 C)
Powerful Google developer tools for immediate impact! (2023-24 C)
 
Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024Finology Group – Insurtech Innovation Award 2024
Finology Group – Insurtech Innovation Award 2024
 

SHOGUN使ってみました

  • 1. SHOGUN 2011 4 23 9 CV PRML @yasutomo57jp ( @inco_san )
  • 2. SHOGUN 1 SHOGUN 2011 4 23 9 CV PRML @yasutomo57jp ( @inco_san )
  • 5.
  • 6.
  • 9. • SHOGUN • 1 SHOGUN • Static Interface
  • 10. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN
  • 11. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface
  • 12. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( )
  • 13. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 14. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 15. • SHOGUN • 1 SHOGUN • Static Interface • 2 SHOGUN • Modular Interface • 3 C++ ( ) • libshogun
  • 18. SHOGUN • • SVM !
  • 19. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT
  • 20. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel
  • 21. SHOGUN • • SVM ! • SVM OCAS, Liblinear, LibSVM, SVMLight, SVMLin, GPDT • Linear, Polynomial, Gaussian and Sigmoid Kernel •
  • 22. SHOGUN • SVM !! • LDA : Linear Discriminant Analysis • LPM : Linear Programming Machine • (Kernel) Perceptron • HMM
  • 26.
  • 31. Q. C++
  • 32.
  • 33.
  • 36.
  • 37.
  • 38.
  • 39.
  • 40. • Static Interface • • • Modular Interface • Python Octave • • libshogun • C++ •
  • 41. • Static Interface • • • Modular Interface • Python Octave • • libshogun • C++ •
  • 42.
  • 43.
  • 45. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases
  • 46. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun
  • 47. Windows Linux (Ubuntu) Cygwin sudo apt-get install shogun http://www.shogun-toolbox.org/#releases Mac sudo port install shogun OK
  • 48.
  • 49. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 50. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 51. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 52. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 53. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 54. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 55. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 56. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 57. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 58. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 59. SVM •• libsvm (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 60. •• libsvm (Cmdline ) set_kernel SIGMOID REAL 50 3 0 (cache, gamma, coeff) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIBSVM libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 61. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 62. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 63. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 64. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 65. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 66. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 67. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 68. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 69. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 70. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify
  • 71. SVM •• svmlight (Cmdline ) set_kernel GAUSSIAN REAL 10 1.2 (cache, kernel width) set_features TRAIN ../data/fm_train_real.dat set_labels TRAIN ../data/label_train_twoclass.dat new_classifier LIGHT libsvm c1 C 1 train_classifier SVM save_classifier libsvm.model load_classifier libsvm.model LIBSVM set_features TEST ../data/fm_test_real.dat out.txt=classify out.txt
  • 72. Python • sg ( from sg import sg ) • sg OK • Cmdline set_feature TEST data.dat • Python sg(‘set_feature’, ‘TEST’, ‘data.dat’) http://www.shogun-toolbox.org/doc/static_tutorial.html
  • 73.
  • 76. • SHOGUN •3 • Static Interface,Modular Interface, libshogun
  • 77. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface
  • 78. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface •
  • 79. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface •
  • 80. • SHOGUN •3 • Static Interface,Modular Interface, libshogun • Static Interface • Modular Interface

Notas do Editor

  1. \n
  2. \n
  3. \n
  4. \n
  5. \n
  6. \n
  7. \n
  8. \n
  9. \n
  10. \n
  11. \n
  12. \n
  13. \n
  14. \n
  15. \n
  16. \n
  17. \n
  18. \n
  19. \n
  20. \n
  21. \n
  22. \n
  23. \n
  24. \n
  25. \n
  26. \n
  27. \n
  28. \n
  29. \n
  30. \n
  31. \n
  32. \n
  33. \n
  34. \n
  35. \n
  36. \n
  37. \n
  38. \n
  39. \n
  40. \n
  41. \n
  42. \n
  43. \n
  44. \n
  45. \n
  46. \n
  47. \n
  48. \n
  49. \n
  50. \n
  51. \n
  52. \n
  53. \n
  54. \n
  55. \n
  56. \n
  57. \n
  58. \n
  59. \n
  60. \n
  61. \n
  62. \n
  63. \n
  64. \n
  65. \n
  66. \n
  67. \n
  68. \n
  69. \n
  70. \n
  71. \n
  72. \n
  73. \n
  74. \n
  75. \n
  76. \n
  77. \n
  78. \n