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.
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Patent
Support vector method for function estimation
TL;DR: In this paper, a method for estimating a real function that describes a phenomenon occurring in a space of any dimensionality is disclosed, where the function is estimated by taking a series of measurements of the phenomenon being described and using those measurements to construct an expansion that has a manageable number of terms.
Book ChapterDOI
The Support Vector Method
TL;DR: The general idea of the Support Vector method is described and theorems demonstrating that the generalization ability of the SV method is based on factors which classical statistics do not take into account are presented.
Proceedings Article
Transductive Inference for Estimating Values of Functions
TL;DR: This direct way for estimating values of the regression (or classification in pattern recognition) can be more accurate than the traditional one based on two steps, first estimating the function and then calculating the values of this function at the points of interest.
Book ChapterDOI
Methods of Pattern Recognition
TL;DR: To implement the SRM inductive principle in learning algorithms one has to minimize the risk in a given set of functions by controlling two factors: thevalue of the empirical risk and the value of the confidence interval.
Proceedings Article
A Support Vector Method for Clustering
TL;DR: A novel method for clustering using the support vector machine approach, which works by separating clusters according to valleys in the underlying probability distribution, and thus clusters can take on arbitrary geometrical shapes.