'Mistake bound' diaporamas de présentation

A New Linear-threshold Algorithm

475 views • 36 slides

7. Computational Learning Theory

7. Computational Learning Theory. ???. ??. Introduction PAC Sample Complexity for Finite Hypothesis Spaces for Infinite Hypothesis Spaces Mistake Bound Model. 7.1 Introduction. Machine Learning ???? ???? ?? ????? ???? ????? ??? ???? ??? ? ????

359 views • 26 slides

Mistake Bounds

Mistake Bounds. William W. Cohen. One simple way to look for interactions. Scan thru data: whenever we see x with y we increase g( x,y )-g( x,~y ) whenever we see x with ~ y we decrease g( x ,y )-g( x,~y )

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Machine Learning

Machine Learning. Lecture 13: Computational Learning Theory. Overview. Are there general laws that govern learning? Sample Complexity: How many training examples are needed to learn a successful hypothesis?

567 views • 18 slides

Machine Learning: Lecture 8

Machine Learning: Lecture 8. Computational Learning Theory (Based on Chapter 7 of Mitchell T.., Machine Learning, 1997). Overview. Are there general laws that govern learning?

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Machine Learning: Lecture 8

Machine Learning: Lecture 8. Computational Learning Theory (Based on Chapter 7 of Mitchell T.., Machine Learning, 1997). Overview. Are there general laws that govern learning?

135 views • 12 slides