Support-vector-regression

Support-vector-regression - Support Vector Regression...

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upport Vector Regression Support Vector Regression • Interesting history: – Generalized Portrait algorithm (Vapnik and Lerner, 1963, Vapnik and Chervonekis (1963, 1974). Russia. roperties of learning machines which enable them to Properties of learning machines which enable them to generalize well to unseen data – Support Vector Machine. Bell Labs. Vapnik and oworkers (1992 on) Optical character recognition coworkers (1992 on). Optical character recognition and object recognition. – Application to regression since 1997. • First part of lecture based on Smola and Scholkopf (2004), see reference material.
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Basic idea e do not care about error below epsilon, but we We do not care about error below epsilon, but we care about magnitude of polynomial coefficients
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Linear approximation • If noise limit is known and function is exactly linear • Can define following optimization problem • Objective function reflects “flatness” roblem may not be feasible Problem may not be feasible
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“Soft” formulation • Primal problem • Second term in objective corresponds to - insensitive loss function
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Optimality criteria • Lagrangian function • Karush-Kuhn-Tucker conditions and
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This note was uploaded on 06/06/2011 for the course EAS 4240 taught by Professor Peterifju during the Spring '08 term at University of Florida.

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Support-vector-regression - Support Vector Regression...

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