The Information Structuralist

ECE 299: learning-theoretic bounds for vector quantizers; binary classification

Posted in Corrupting the Young, Statistical Learning and Inference by mraginsky on March 29, 2011

More learning-theoretic goodness:

  • Case study: empirical quantizer design, where I discuss beautiful work by Tamás Linder et al. that uses VC theory to bound the performance of empirically designed vector quantizers (which is engineering jargon for consistency of the method of k-means).
  • Binary classification: from the classic bounds for linear and generalized linear discriminant rules to modern techniques based on surrogate losses; voting methods; kernel machines; Convex Risk Minimization.
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