1. DATA MINING AND MACHINE LEARNING
IN A NUTSHELL
ACTIVE LEARNING
Mohammad-Ali Abbasi
http://www.public.asu.edu/~mabbasi2/
SCHOOL OF COMPUTING, INFORMATICS, AND DECISION SYSTEMS ENGINEERING
ARIZONA STATE UNIVERSITY
Arizona State University
http://dmml.asu.edu/
Data Mining and Machine Learning Lab
Data Mining and Machine Learning- in a nutshell Active Learning 1

2. What is Active Learning
The process of guiding the
sampling process by querying for
certain types of instances based
upon the data that we have seen
so far
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3. The importance of Active learning
• The process of labeling the training data
is not only time-consuming but also very
expensive
• Less training data we need, more we will
save
Arizona State University
Data Mining and Machine Learning Lab
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4. Applications
• Text classification
• Web page classification
• Junk mail recognition
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5. Methods
• Neural Networks
• Bayesian rule
• SVM
No matter which method will be used, the core problem will be the same
•The core problem is how to select training points actively?
•In other words, which training points will be informative to the model?
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6. Active vs. Passive Learning
• Passive:
– Randomly select training samples
• Active
– Use an automated method to select training
samples
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7. References
• Theory, Methods and Applications of Active
Learning
– Robert Nowak, Department of Electrical and
Computer Engineering, University of Wisconsin -
Madison
– Rui Castro, Department of Electrical
Engineering, Columbia University
– Published: July 30, 2009, recorded: June 2009
– http://videolectures.net/mlss09us_nowak_castro_tmaal/
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8. Mohammad-Ali Abbasi (Ali),
Ali, is a Ph.D student at Data Mining
and Machine Learning Lab, Arizona
State University.
His research interests include Data
Mining, Machine Learning, Social
Computing, and Social Media Behavior
Analysis.
http://www.public.asu.edu/~mabbasi2/
Arizona State University
Data Mining and Machine Learning Lab
Data Mining and Machine Learning- in a nutshell Active Learning 8