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Explore the application of machine learning in face detection, classification of diseases like cancer, and historic document analysis. Learn about supervised learning approaches such as Nearest Neighbor and k-Nearest Neighbor for predictive modeling.
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Machine Learning • Definition 1 • “The subfield of AI concerned with programs that learn from experience” • Russell / Norvig, AIMA • Definition 2 • “the application of induction algorithms, which is one step in the knowledge discovery process.” • Machine Learning definition in glossary from Machine Learning at http://robotics.stanford.edu/~ronnyk/glossary.html
Supervised Learning Classification • Example: Cancer diagnosis Training Set • Use this training set to learn how to classify patients where diagnosis is not known: Test Set Input Data Classification • The input data is often easily obtained, whereas the classification is not.
Classification Problem • Goal: Use training set + some learning method to produce a predictive model. • Use this predictive model to classify new data. • Sample applications:
Application: Breast Cancer Diagnosis Research by Mangasarian,Street, Wolberg
Breast Cancer Diagnosis Separation Research by Mangasarian,Street, Wolberg
Application: Document Classification • The Federalist Papers • Written in 1787-1788 by Alexander Hamilton, John Jay, and James Madison to persuade residents of the State of New York to ratify the U.S. Constitution • All written under the pseudonym “Publius” • Who wrote which of them? • Hamilton wrote 56 papers • Madison wrote 50 papers • 12 disputed papers, generally understood to be written by Hamilton or Madison, but not known which Research by Bosch, Smith
Federalist Papers Classification Graphic by Fung Research by Bosch, Smith
Application: Face Detection • Training data is a collection of Faces and NonFaces • Rotation and Mirroring added in to provide robustness Image obtained from work by Osuna, Freund, and Girosi athttp://www.ai.mit.edu/projects/cbcl/res-area/object-detection/face-detection.html
Face Detection Results Image obtained from work by Osuna, Freund, and Girosi athttp://www.ai.mit.edu/projects/cbcl/res-area/object-detection/face-detection.html
Nearest Neighbor • Simple effective approach for supervised learning problems • Envision each example as a point in n-dimensional space • Picture with 2 of them • Classify test point same as nearest training point (Euclidean distance)
k-Nearest Neighbor • Nearest Neighbor can be subject to noise • Incorrectly classified training points • Training anomalies • k-Nearest Neighbor • Find k nearest training points (k odd) and vote on which classification • Training time? • Testing time? • Works on numerical data