Candidate Elimination - CEDAR

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Unbiased Learner is Too Limited • S boundary is always disjunction of positive examples • G boundary is always disjunction of negative examples • No generalization: only examples unambiguously classified by S and G are the training examples themselves • Every single instance of X has to be presented! 40
The Futility of Bias-Free Learning • Fundamental property of inductive inference: A learner that makes no a priori assumptions regarding the identity for the target concept has no rational basis for classifying any unseen instances. • Inductive learning requires some form of prior assumptions or inductive bias. 41
Page 1 and 2: Concept Learning Space of Versions
Page 3 and 4: FIND-S Algorithm : Finding a Maxima
Page 5 and 6: Hypothesis Space Search Performed b
Page 7 and 8: Representation • The CANDIDATE-EL
Page 9 and 10: Version Space: Definition • The s
Page 11 and 12: The LIST-THEN-ELIMINATE Algorithm
Page 13 and 14: Version Space with General (G) and
Page 15 and 16: Specific Boundary Definition: The s
Page 17 and 18: The CANDIDATE-ELIMINATION Algorithm
Page 19 and 20: An Illustrative Example • We will
Page 21 and 22: CANDIDATE-ELIMINATION Trace (Sample
Page 23 and 24: Final Version Space for EnjoySport
Page 25 and 26: 25 Remarks on Version Spaces and CA
Page 27 and 28: What Training Example Should the Le
Page 29 and 30: Optimal query strategy • The opti
Page 31 and 32: New Instances To Be Classified Inst
Page 33 and 34: A Biased Hypothesis Space Example S
Page 35 and 36: An Unbiased Learner • EnjoySport
Page 37 and 38: Conjunctive concepts for 3 binary v
Page 39: An Unbiased Learner • New hypothe
Page 43 and 44: Inductive Bias Definition 43 •Con
Page 45 and 46: Modeling Inductive Systems by Equiv
Page 47 and 48: Summary • Concept learning can be
Page 49: Summary, continued • If the hypot

Candidate Elimination - CEDAR

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