1 / 8

Ch. 19 – Knowledge in Learning

Ch. 19 – Knowledge in Learning. Supplemental slides for CSE 327 Prof. Jeff Heflin. Current Best Hypothesis Search.

jameson-carlson
Download Presentation

Ch. 19 – Knowledge in Learning

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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 hypothesisH any hypothesis consistent with the first example in examplesfor each remaining example in examplesdo ife is false positive for HthenH  choose a specialization of H consistent with examples else ife is false negative for HthenH  choose a generalization of H consistent with examplesif no consistent specialization/generalization can be found then failreturnH Note: here chooseis 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 (current best hypothesis search) Training Set Only consider candidate definitions that are positive conjunctive sentences

  4. Current-Best Hypothesis Search 1 True hypothesis X1: ok example: status 2 FP = false positive True FN = false negative X2: FP 3 Color(x,Red) Shape(x,Circle) X3: FP X3: ? 4 6 Color(x,Red)  Size(x,Large) Color(x,Red)  Shape(x,Circle) X4: ok X4: ok 5 Color(x,Red)  Size(x,Large) 7 Color(x,Red)  Shape(x,Circle) X5: FN X5: ok

  5. Version Space Learning function VERSION-SPACE-LEARNING(examples)returns a version spacelocal variables:V, the version space (the set of all hypotheses) V the set of all hypothesesfor each example e in examplesdo ifV is not empty thenV  VERSION-SPACE-UPDATE(V,e)returnV function VERSION-SPACE-UPDATE(V,e)returns an updated version space V {h V: h is consistent with e}returnV From Figure 19.3, p. 683

  6. Ordering on Hypothesis Space P(x) Q(x) R(x) P(x)  Q(x) P(x)  R(x) Q(x)  R(x) P(x)  Q(x)  R(x)

  7. Version Space Update Details function VERSION-SPACE-UPDATE(G,S,e)returns an updated G-set and S-set (version space) for eachg in Gife is a false positive for g G  G – gG  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 ife is a false negative for g G  G – gfor eachs in Sife is a false positive for s S  S – selse ife is a false negative for s S  S – sS  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} returnG,S

  8. Example Learning Problem (version space learning) Training Set Only consider candidate definitions that are positive conjunctive sentences

More Related