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Exploring Machine Learning: Definitions, Examples, and Applications

Dive into the world of machine learning with definitions, examples such as cancer diagnosis and document classification, and applications like face detection using algorithms such as Nearest Neighbor and k-Nearest Neighbor. Understand the process of supervised learning, classification problems, and the use of training sets to create predictive models for various real-world scenarios.

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Exploring Machine Learning: Definitions, Examples, and Applications

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  1. 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

  2. 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.

  3. Classification Problem • Goal: Use training set + some learning method to produce a predictive model. • Use this predictive model to classify new data. • Sample applications:

  4. Application: Breast Cancer Diagnosis Research by Mangasarian,Street, Wolberg

  5. Breast Cancer Diagnosis Separation Research by Mangasarian,Street, Wolberg

  6. 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

  7. Federalist Papers Classification Graphic by Fung Research by Bosch, Smith

  8. 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

  9. Face Detection Results Image obtained from "Support Vector Machines: Training and Applications" by Osuna, Freund, and Girosi.

  10. 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

  11. 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)

  12. 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

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