V
Vladimir Vapnik
Researcher at Princeton University
Publications - 101
Citations - 170176
Vladimir Vapnik is an academic researcher from Princeton University. The author has contributed to research in topics: Support vector machine & Generalization. The author has an hindex of 59, co-authored 101 publications receiving 159214 citations. Previous affiliations of Vladimir Vapnik include Facebook & Columbia University.
Papers
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Journal ArticleDOI
Comparing support vector machines with Gaussian kernels to radial basis function classifiers
Bernhard Schölkopf,Kah-Kay Sung,C.J.C. Burges,Federico Girosi,Partha Niyogi,Tomaso Poggio,Vladimir Vapnik +6 more
TL;DR: The results show that on the United States postal service database of handwritten digits, the SV machine achieves the highest recognition accuracy, followed by the hybrid system, and the SV approach is thus not only theoretically well-founded but also superior in a practical application.
Proceedings Article
Feature Selection for SVMs
Jason Weston,Sayan Mukherjee,Olivier Chapelle,Massimiliano Pontil,Tomaso Poggio,Vladimir Vapnik +5 more
TL;DR: The resulting algorithms are shown to be superior to some standard feature selection algorithms on both toy data and real-life problems of face recognition, pedestrian detection and analyzing DNA microarray data.
Book ChapterDOI
Predicting Time Series with Support Vector Machines
Klaus-Robert Müller,Alexander J. Smola,Gunnar Rätsch,Bernhard Schölkopf,Jens Kohlmorgen,Vladimir Vapnik +5 more
TL;DR: Two different cost functions for Support Vectors are made use: training with an e insensitive loss and Huber's robust loss function and how to choose the regularization parameters in these models are discussed.
Proceedings Article
Principles of Risk Minimization for Learning Theory
TL;DR: Systematic improvements in prediction power and empirical risk minimization are illustrated in application to zip-code recognition.