Candidate Elimination - CEDAR

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16 Version Space Theorem Version space consists of hypotheses contained in S, plus those contained in G and those in-between Theorem: Let X be an arbitrary set of instances, and let H be a set of boolean-valued hypotheses defined over X. Let c : X → {0,1} be an arbitrary target concept defined over X, and let D be an arbitrary set of training examples {〈x,c(x)〉}. For all X, H, c, and D such that S and G are well defined, VS H , D = { h ∈ H | ( ∃s ∈ S)( ∃g ∈G)( g ≥ g h ≥ g s)}
The CANDIDATE-ELIMINATION Algorithm • The CANDIDATE-ELIMINATION algorithm computes the version space containing all hypotheses from H that are consistent with an observed sequence of training examples. 17
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: Specific Boundary Definition: The s
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 and 40: An Unbiased Learner • New hypothe
Page 41 and 42: The Futility of Bias-Free Learning
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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