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Using binary classifiers

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Machine Learning A large and fascinating field; there’s much more than what you’ll see in this class!
What should we try to learn, if we want to… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
What should we try to learn, if we want to… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],This stuff has got to be an important part of the future … … beats trying to program all the special cases directly … and there are “intelligent” behaviors you can’t imagine programming directly. (Most of the stuff now in your brain wasn’t programmed in advance, either!)
The simplest problem: Supervised binary classification of vectors ,[object Object],[object Object],[object Object],[object Object]
Linear Separators slide thanks to Ray Mooney
Linear Separators slide thanks to Ray Mooney
Nonlinear Separators slide thanks to Ray Mooney (modified)
Nonlinear Separators Note: A more complex function requires more data to generate an accurate model (sample complexity) slide thanks to Kevin Small (modified) x y
Encoding and decoding for learning ,[object Object],[object Object]
Features for recognizing a chair?
Features for recognizing childhood autism? (from DSM IV, the Diagnostic and Statistical Manual) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Features for recognizing childhood autism? (from DSM IV, the Diagnostic and Statistical Manual) ,[object Object],[object Object],[object Object],[object Object],[object Object]
Features for recognizing a prime number? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Features for recognizing masculine vs. feminine words in French? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Features for recognizing when the user who’s typing isn’t the usual user? ,[object Object]
Measuring performance ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Encoding and decoding for learning ,[object Object],[object Object],[object Object]
Multiclass Classification Many binary classifiers (“one versus all”) slide thanks to Kevin Small (modified) One multiway classifier
Regression: predict a number, not a class ,[object Object],[object Object]
Inference: Predict a whole pattern ,[object Object],[object Object],[object Object],[object Object],[object Object]
Defining Learning Problems ,[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified)
Context Sensitive Spelling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified)
Sentence Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified)
Sentence Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified) 1 2 3
Embedding ,[object Object],[object Object],slide thanks to Kevin Small (modified) Peace Piece
Sparse Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified)
Types of Sparsity ,[object Object],[object Object],[object Object],[object Object],[object Object],slide thanks to Kevin Small (modified)
Training paradigms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Training and test sets ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Overfitting and underfitting ,[object Object],[object Object],[object Object],[object Object]
“ Feature Engineering” Workshop in 2005 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
“ Feature Engineering” Workshop in 2005 ,[object Object],[object Object],[object Object],[object Object]
“ Feature Engineering” Workshop in 2005 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
A Machine Learning System slide thanks to Kevin Small (modified) Preprocessing Raw Text Formatted Text
Preprocessing Text ,[object Object],[object Object],slide thanks to Kevin Small (modified) They recently recovered a small piece of a live Elvis concert recording. He was singing gospel songs, including “Peace in the Valley.” 0 0 0 They 0 0 1 recently 0 0 2 recovered 0 0 3 a 0 0 4 small piece 0 5 piece 0 0 6 of : 0 1 6 including 0 1 7 QUOTE peace 1 8 Peace 0 1 9 in 0 1 10 the 0 1 11 Valley 0 1 12 . 0 1 13 QUOTE
A Machine Learning System Feature Vectors slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Raw Text Formatted Text
Feature Extraction ,[object Object],[object Object],slide thanks to Kevin Small (modified) 0 0 0 They 0 0 1 recently 0 0 2 recovered 0 0 3 a 0 0 4 small piece 0 5 piece 0 0 6 of : 0 1 6 including 0 1 7 QUOTE peace 1 8 Peace 0 1 9 in 0 1 10 the 0 1 11 Valley 0 1 12 . 0 1 13 QUOTE 0, 1001, 1013, 1134, 1175, 1206 1, 1021, 1055, 1085, 1182, 1252 Lexicon File
A Machine Learning System Testing Examples Feature Vectors Training Examples slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Machine Learner Classifier Postprocessing Raw Text Formatted Text Function Parameters Labels Annotated Text
A Machine Learning System Testing Examples Feature Vectors Training Examples slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Machine Learner Classifier Postprocessing Raw Text Formatted Text Function Parameters Labels Annotated Text

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Using binary classifiers

  • 1. Machine Learning A large and fascinating field; there’s much more than what you’ll see in this class!
  • 2.
  • 3.
  • 4.
  • 5. Linear Separators slide thanks to Ray Mooney
  • 6. Linear Separators slide thanks to Ray Mooney
  • 7. Nonlinear Separators slide thanks to Ray Mooney (modified)
  • 8. Nonlinear Separators Note: A more complex function requires more data to generate an accurate model (sample complexity) slide thanks to Kevin Small (modified) x y
  • 9.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 16.
  • 17.
  • 18. Multiclass Classification Many binary classifiers (“one versus all”) slide thanks to Kevin Small (modified) One multiway classifier
  • 19.
  • 20.
  • 21.
  • 22.
  • 23.
  • 24.
  • 25.
  • 26.
  • 27.
  • 28.
  • 29.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34. A Machine Learning System slide thanks to Kevin Small (modified) Preprocessing Raw Text Formatted Text
  • 35.
  • 36. A Machine Learning System Feature Vectors slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Raw Text Formatted Text
  • 37.
  • 38. A Machine Learning System Testing Examples Feature Vectors Training Examples slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Machine Learner Classifier Postprocessing Raw Text Formatted Text Function Parameters Labels Annotated Text
  • 39. A Machine Learning System Testing Examples Feature Vectors Training Examples slide thanks to Kevin Small (modified) Preprocessing Feature Extraction Machine Learner Classifier Postprocessing Raw Text Formatted Text Function Parameters Labels Annotated Text

Notas do Editor

  1. Add function formula
  2. pictures of a 4 class classifier...with the OvA definition
  3. Well-posed learning problems
  4. Framing the classification task
  5. note that the sentence changed
  6. note that the sentence changed
  7. Text categorization
  8. Word tagging
  9. Without algorithmic details as Dan says
  10. A machine learning system
  11. Preprocessing text
  12. A machine learning system
  13. Feature Generation (Kernels)
  14. A machine learning system
  15. A machine learning system