Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Support Vector Clustering Asa Ben-Hur (2008)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Large Margin vs. Large Volume in Transductive Learning (2008)
El-Yaniv, Ran, Pechyony, Dmitry, Vapnik, Vladimir
We consider a large volume principle for transductive learn- ing that prioritizes the transductive equivalence classes according to the volume they occupy in hypothesis space. We approximate volume...
On the Effective VC Dimension L'eon Bottou, (2007)
Corinna Cortes, Vladimir Vapnik
The very idea of an "Effective Vapnik Chervonenkis (VC) dimension" (Vapnik, Levin and Le Cun, 1993) relies on the hypothesis that the relation between the generalization error and...
1 SVMs for Histogram-Based Image Classification (2007)
Olivier Chapelle, Patrick Haffner, Vladimir Vapnik
Abstract--- Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector...
Vladimir Vapnik, Olivier Chapelle
We introduce the concept of span of support vectors (SV) and show that the generalization ability of support vector machines (SVM) depends on this new geometrical concept. We prove that the value of...
Olivier Chapelle, Vladimir Vapnik, Jason Weston
We introduce an algorithm for estimating the values of a function at a set of test points x
Olivier Chapelle, Vladimir Vapnik
New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that...
Alexander Smola, Chris Burges, Harris Drucker, Steve Golowich, Leo Van Hemmen, Klaus-robert Muller, ...
Version 1.01
Harris Drucker, Chris J. C, Burges* Linda Kaufman, Alex Smola, Vladimir Vapnik
A new regression technique based on Vapnik's concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on...
Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of...
Olivier Chapelle, Patrick Haffner, Vladimir Vapnik
Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that Support Vector Machines (SVM) can...
On the Effective VC Dimension L'eon Bottou, (2007)
Corinna Cortes, Vladimir Vapnik
The very idea of an "Effective Vapnik Chervonenkis (VC) dimension" (Vapnik, Levin and Le Cun, 1993) relies on the hypothesis that the relation between the generalization error and...
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Support Vector Clustering Asa Ben-Hur (2007)
Biowulf Technologies, David Horn, Hava T. Siegelmann, Vladimir Vapnik, Nello Critianini, John Shawe-taylor, ...
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary...
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case of binary...
Inference with the universum (2006)
Jason Weston, Ronan Collobert, Fabian Sinz, Léon Bottou, Vladimir Vapnik
In this paper we study a new framework introduced by Vapnik (1998) that is an alternative capacity concept to the large margin approach. In the particular case of binary classification, we are given...
Parallel support vector machines: The cascade svm (2005)
Hans Peter Graf, Eric Cosatto, Leon Bottou, Igor Durdanovic, Vladimir Vapnik
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing...
Parallel support vector machines: The cascade svm (2005)
Hans Peter Graf, Eric Cosatto, Leon Bottou, Igor Durdanovic, Vladimir Vapnik
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing...
Choosing multiple parameters for support vector machines (2002)
Olivier Chapelle, Vladimir Vapnik, Olivier Bousquet, Sayan Mukherjee
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of...
Model selection for small sample regression (2002)
Olivier Chapelle, Vladimir Vapnik, Yoshua Bengio
yy Universite de Montreal, Canada
Support vector clustering (2001)
Asa Ben-hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
We present a novel clustering method using the approach of support vector machines. Data points are mapped by means of a Gaussian kernel to a high dimensional feature space, where we search for the...
A support vector method for clustering (2001)
Asa Ben-hur, David Horn, Hava T. Siegelmann, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
Vicinal risk minimization (2001)
Olivier Chapelle, Jason Weston, L Eon Bottou, Vladimir Vapnik
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural Risk Minimization Principle such as Support Vector Machines or...
A Support Vector Method for Hierarchical Clustering (2001)
Asa Ben-hur, David Horn, Hava Siegelmann, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
Feature Selection for SVMs (2001)
J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, Tomaso Poggio, Vladimir Vapnik
We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leave-one-out error, which can be efficiently...
A support vector method for clustering (2001)
Asa Ben-hur, Hava T. Siegelmann, David Horn, Vladimir Vapnik
We present a novel method for clustering using the support vector machine approach. Data points are mapped to a high dimensional feature space, where support vectors are used to define a sphere...
Multivariate density estimation: A support vector machine approach (2000)
Sayan Mukherjee, Vladimir Vapnik
This publication can be retrieved by anonymous ftp to publications.ai.mit.edu. The pathname for this publication is: ai-publications/1500-1999/AIM-1653.ps A Support Vector Machine (SVM) algorithm for...
Model Selection for Support Vector Machines (2000)
Olivier Chapelle, Vladimir Vapnik
New functionals for parameter (model) selection of Support Vector Machines are introduced based on the concepts of the span of support vectors and rescaling of the feature space. It is shown that...
Multivariate Density Estimation: An SVM Approach (1999)
Mukherjee, Sayan, Vapnik, Vladimir
We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for...
Multivariate Density Estimation: An SVM Approach (1999)
Mukherjee, Sayan, Vapnik, Vladimir
We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for...
Prior Knowledge in Support Vector Kernels (1998)
Bernhard Schölkopf, Bernhard Sch Olkopf, Patrice Simard, Alex Smola, Vladimir Vapnik
We explore methods for incorporating prior knowledge about a problem at hand in Support Vector learning machines. We show that both invariances under group transformations and prior knowledge about...
Support Vector Methods in Learning and Feature Extraction (1998)
Bernhard Schölkopf, Alex Smola, Klaus-Robert Müller, Chris Burges, Vladimir Vapnik
The last years have witnessed an increasing interest in Support Vector (SV) machines, which use Mercer kernels for efficiently performing computations in high-dimensional spaces. In pattern...
Prior Knowledge in Support Vector Kernels (1998)
Bernhard Schölkopf, Bernhard Sch Olkopf, Biologische Kybernetik, Patrice Simard, Vladimir Vapnik, Alexander J. Smola
We explore methods for incorporating prior knowledge about a problem at hand in Support Vector learning machines. We show that both invariances under group transformations and prior knowledge about...
Support vector regression machines (1997)
Harris Drucker, Chris J. C, Burges* Linda Kaufman, Alex Smola, Vladimir Vapnik
A new regression technique based on Vapnik’s concept of support vectors is introduced. We compare support vector regression (SVR) with a committee regression technique (bagging) based on regression...
Support Vector Regression with ANOVA Decomposition Kernels (1997)
Mark O. Stitson, Alex Gammerman, Vladimir Vapnik, Volodya Vovk, Chris Watkins, Jason Weston, ...
Support Vector Machines using ANOVA Decomposition Kernels (SVAD) [Vapng] are a way of imposing a structure on multi-dimensional kernels which are generated as the tensor product of one-dimensional...
Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers (1997)
Bernhard Schölkopf, Kah-Kay Sung, Chris Burges, Federico Girosi, Partha Niyogi, Tomaso Poggio, ...
The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF)...
Vladimir Vapnik, Steven E. Golowich, Alex Smola
The Support Vector (SV) method was recently proposed for estimating regressions, constructing multidimensional splines, and solving linear operator equations [Vapnik, 1995]. In this presentation we...
Incorporating Invariances in Support Vector Learning Machines (1996)
Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
. Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding...
Incorporating Invariances in Support Vector Learning Machines (1996)
Bernhard Scholkopf, Chris Burges, Vladimir Vapnik
. Developed only recently, support vector learning machines achieve high generalization ability by minimizing a bound on the expected test error; however, so far there existed no way of adding...
Support Vector Networks (1995)
Corinna Cortes, Vladimir Vapnik
Abstract. The support-vector network is a new learning machine for two-group classification problems. The machine conceptually implements the following idea: input vectors are non-linearly mapped to...
Learning algorithms for classification: A comparison on handwritten digit recognition (1995)
Yann Lecun, L. D. Jackel, N Bottou, Corinna Cartes, John S. Denker, Harris Drucker, ...
This paper compare the performance of se\-era1 classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and...
Learning Algorithms For Classification: A Comparison On Handwritten Digit Recognition (1995)
Yann Lecun, L. D. Jackel, Léon Bottou, Corinna Cortes, John S. Denker, Harris Drucker, ...
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and...
Extracting Support Data for a Given Task (1995)
Bernhard Schölkopf, Chris Burges, Vladimir Vapnik
We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve a given classification task: using the Support Vector Algorithm to...
Att Bell Laboratories, Yann Lecun, L. D. Jackel, Leon Bottou, Corinna Cortes, John S. Denker, ...
This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also training time, recognition time, and...
Measuring the VC-dimension of a Learning Machine (1994)
Vladimir Vapnik, Esther Levin, Yann Le Cun
A method for measuring the capacity of learning machines is described. The method is based on fitting a theoretically derived function to empirical measurements of the maximal difference between the...
Local Learning Algorithms (1992)
Very rarely are training data evenly distributed in the input space. Local learning algorithms attempt to locally adjust the capacity of the training system to the properties of the training set in...