Ch. 19 – Knowledge in Learning

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 Training Set Only consider candidate definitions that are positive conjunctive sentences

4. 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

5. 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}ife is a false negative for g G  G – gfor eachs in Sife is a false positive for s S  S – sife 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

6. Example Learning Problem Training Set Only consider candidate definitions that are positive conjunctive sentences