Download presentation

Presentation is loading. Please wait.

Published byShayna Seaman Modified over 2 years ago

1
Ch. 19 – Knowledge in Learning Supplemental slides for CSE 327 Prof. Jeff Heflin

2
Current Best Hypothesis Search function CURRENT-BEST-LEARNING(examples) returns a hypothesis H any hypothesis consistent with the first example in examples for each remaining example in examples do if e is false positive for H then H choose a specialization of H consistent with examples else if e is false negative for H then H choose a generalization of H consistent with examples if no consistent specialization/generalization can be found then fail return H Note: here choose is a special operator that allows you to backtrack to a previous choice and select another option when the search fails. An actual implementation would probably use depth-first search instead. From Figure 19.2, p. 681

3
Example Learning Problem ExampleDescriptionsClassifications X1X1 Color(X 1,Red) Size(X 1,Large) Shape(X 1,Circle) Q(X 1 ) X2X2 Color(X 2,Blue) Size(X 2,Small) Shape(X 2,Square) Q(X 2 ) X3X3 Color(X 3,Red) Size(X 3, Small) Shape(X 3,Square) Q(X 3 ) X4X4 Color(X 4,Green) Size(X 4,Large) Shape(X 4,Triangle) Q(X 4 ) X5X5 Color(X 5,Red) Size(X 5,Small) Shape(X 5,Circle) Q(X 5 ) Only consider candidate definitions that are positive conjunctive sentences Training Set

4
Version Space Learning function VERSION-SPACE-LEARNING(examples) returns a version space local variables: V, the version space (the set of all hypotheses) V the set of all hypotheses for each example e in examples do if V is not empty then V VERSION-SPACE-UPDATE(V,e) return V function VERSION-SPACE-UPDATE(V,e) returns an updated version space V {h V: h is consistent with e} return V From Figure 19.3, p. 683

5
Version Space Update Details function VERSION-SPACE-UPDATE(G,S,e) returns an updated G-set and S-set (version space) for each g in G if e is a false positive for g G G – g G G {h : h is the most general specialization of g that is consistent with e and h is more general than some member of S} else if e is a false negative for g G G – g for each s in S if e is a false positive for s S S – s else if e is a false negative for s S S – s S S {h : h is the most specific generalization of s that is consistent with e and h is more specific than some member of G} return G,S

6
Example Learning Problem Only consider candidate definitions that are positive conjunctive sentences Training Set Descriptions Classifications Size(X 1,Large) Shape(X 1,Circle) Color(X 1,Red) Q(X 1 ) Size(X 2,Large) Shape(X 2,Square) Color(X 2,Blue) Q(X 2 ) Size(X 3,Small) Shape(X 3,Circle) Color(X 3,Red) Q(X 3 ) Size(X 4,Small) Shape(X 4,Circle) Color(X 4,Blue) Q(X 4 ) Size(X 5,Large) Shape(X 5,Square) Color(X 5,Red) Q(X 5 )

Similar presentations

OK

Artificial Intelligence University Politehnica of Bucharest 2008-2009 Adina Magda Florea

Artificial Intelligence University Politehnica of Bucharest 2008-2009 Adina Magda Florea

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google

Product mix ppt on nestle pure Ppt on difference between packages and interfaces in java Ppt on tata steel company Ppt on eisenmenger syndrome asd Ppt on central administrative tribunal india E paper display ppt on ipad Stereoscopic image display ppt online Ppt on tsunami and earthquake Ppt on life cycle of silk moth Ppt on shell scripting language