Gram Matrix

Gram Matrix   “gram matrix” – Google Scholar “gram matrix” – Google Search Gram Matrix — From MathWorld “Gram matrix (a matrix of dot products: see (Horn, 1985))” Burges (1998)


Support Vector Machines: Financial Applications   Listed in order of citations per year, highest at the top. Last updated September 2006. PANG, Bo, Lillian LEE and Shivakumar VAITHYANATHAN, 2002. Thumbs up? Sentiment Classification using Machine Learning Techniques, In: EMNLP ’02: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing – Volume 10, pages 79–86. …

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Feature Selection

SVMs and Feature Selection   MUKHERJEE, S., Bioinformatics Applications and Feature Selection for SVMs Mukherjee (2001) NIYOGI, Partha, Chris BURGES and Padma RAMESH, Distinctive Feature Detection Using Support Vector Machines Niyogi, Burges and Ramesh (1998) Support Vector Methods in Learning and Feature Extraction Schölkopf et al. (1998) Feature Selection for SVMs Weston et al.


Duality   Duality (mathematics) – Wikipedia Dual problem – Wikipedia Linear programming: Duality – Wikipedia “dual optimization problem” – Google Scholar “dual optimization problem” – Google Search Mathematical Programming Glossary Page 2

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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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


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   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   EVGENIOU, Theodoros, et al., Bounds on the Generalization Performance of Kernel Machines Ensembles VERRI, Alessandro, 9.520: Class 24: Bagging and Boosting,6,45;journal,1298,2282;linkingpublicationresults,1:105633,1