Machine Learning meets the Real World: Successes and new research directions

1/30/03


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Table of Contents

Machine Learning meets the Real World: Successes and new research directions

Data, data everywhere...

A wealth of information

A wealth of information

Machine learning success (Machine learning is ubiquitous)

Why research in machine learning is so good today

Plan for this talk

Induction of decision trees

Inductive learning

Sample data

Predictive model I.e., g<x>

Learning objectives

TDIDT

Which is better?

The Gain Criterion

Information (Entropy)

Information (Entropy)

Entropy after a split

Information Gain

Which is better?

Scrubber (the success story)

MAX, 1990

Scrubber 2

Scrubber 3

Implementation difficulties

Requirements

Additional requirements (ours)

Phase I: Modeling Scrubber 2

Data

Background knowledge

Phase I results

Phase II: Acceptance

Trading off simplicity and correctness

Phase II results

Phase III: Working toward extensibility

Phase IIIb: More data

Phase III results

Summarizing the success story

Lessons can be learned from success

Lessons can be learned from success

Lessons can be learned from success

Lessons can be learned from success

Lessons can be learned from success

Further reading and acknowledgements

Author: Computer Science Dept

Email: andrea@cs.williams.edu

Home Page: http://www.cs.williams.edu/~andrea

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