Instance Based Learning

This talk is based on

• Tom M. Mitchell. Machine Learning. McGraw Hill. 1997. Chapter 8.
• and his slides.

1. 1 Introduction
2. 2 k-Nearest Neighbor Learning
   1. 2.1 When to Use K-Nearest
   2. 2.2 Voronoi Diagram
   3. 2.3 Behavior in the Limit
   4. 2.4 Distance-Weighted kNN
   5. 2.5 Curse of Dimensionality
3. 3 Notation
4. 4 Locally Weighted Regression
   1. 4.1 Local Regression Descent
5. 5 Radial Basis Functions
   1. 5.1 Radial Basis Function Networks
6. 6 Case-Based Reasoning
   1. 6.1 CADET
   2. 6.2 CADET Answers
   3. 6.3 CADET vs. k-Nearest
   4. 6.4 CBR Summary
7. 7 Lazy and Eager Learning

Entire Presentation with Notes

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Copyright © 2009 José M. Vidal . All rights reserved.

04 March 2003, 11:13AM