Support Vector Machines

Domain Knowledge

Prior Domain Knowledge   Home Barzilay and Brailovsky (1999) “An approach to constructing a kernel function which takes into account some domain knowledge about a problem and thus essentially diminishes the number of noisy parameters in high dimensional feature space is suggested.” Chapelle (2001) and Sch?o?lkopf “The choice of an SVM kernel corresponds to the …

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Convexity

Convex stuff   Convex – Wikipedia Convex — From MathWorld Convex combination – Wikipedia Convex function – Wikipedia Convex Function — From MathWorld Convex hull – Wikipedia Convex Hull — From MathWorld Convex Set — From MathWorld Convex optimization – Wikipedia Convex Optimization Theory — From MathWorld

Classification

SVMs for Classification   BOSER, Bernhard E., Isabelle M. GUYON and Vladimir N. VAPNIK, A Training Algorithm for Optimal Margin Classifiers [about 515] “Experimental results on optical character recognition problems demonstrate the good generalization obtained when compared with other learning algorithms.” Boser, Guyon and Vapnik (1992) BURBIDGE, R. et al., Drug design by machine learning: support vector machines …

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Bibliographies

Bibliographies   CRISTIANINI, Nello and John SHAWE-TAYLOR, Support Vector Machines – The Book HERBRICH, Ralf, References PELCKMANS, Kristiaan, Bibliography PRICE, Keith, Keith Price Bibliography Support Vector Machines SCHÖLKOPF, Bernhard, Support Vector Learning SMOLA, Alex and Bernhard SCHÖLKOPF, Publications on Kernel Methods

Bagging

Bagging   EVGENIOU, Theodoros, et al., Bounds on the Generalization Performance of Kernel Machines Ensembles VERRI, Alessandro, 9.520: Class 24: Bagging and Boosting http://www.springerlink.com/(h1mbsw55da535hqotaptnh45)/app/home/contribution.asp?referrer=parent&backto=issue,6,45;journal,1298,2282;linkingpublicationresults,1:105633,1