The Version Space is a hierarchical representation of knowledge that enables you to keep track of all the useful information supplied by a sequence of learning examples without remembering any of the examples. We can express the hard margin SVM classifier in terms of a geometric representation of the version space. For This purpose, we restrict our consideration to hypotheses f (x) = ⟨ W, φ (x) ⟩ without bias (i.e., B = 0). The following results, however, can be extended to SVMs with b ̸ = 0. The version space V (L) refers to the subset of F that includes all hypotheses consistent with the training set L: ...
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