V. Vapnik - Academia.edu (original) (raw)
Papers by V. Vapnik
this report we describe how the Support Vector (SV) technique of solving linearoperator equations... more this report we describe how the Support Vector (SV) technique of solving linearoperator equations can be applied to the problem of density estimation [4]. Wepresent a new optimization procedure and set of kernels closely related to current SVtechniques that guarantee the monotonicity of the approximation. This techniqueestimates densities with a mixture of bumps (Gaussian-like shapes), with the usualSV property that only some coefficients are non-zero. Both the width and theheight of each bump is chosen adaptively ...
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IEEE Transactions on Signal Processing, 1997
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IEEE Transactions on Neural Networks, 1997
Page 1. Statistics for Engineering and Information Science Vladimir N. Vapnik he Nature f Statist... more Page 1. Statistics for Engineering and Information Science Vladimir N. Vapnik he Nature f Statistical earning Theo econd Edition Springer Page 2. Page 3. ... Page 5. Vladimir N. Vapnik The Nature of Statistical Learning Theory Second Edition With 50 Illustrations Springer Page 6. ...
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Computers & Mathematics with Applications, 2001
... Wu, KY Michael Wong and David Bodoff 513 Distance Metric Learning with Application to Cluster... more ... Wu, KY Michael Wong and David Bodoff 513 Distance Metric Learning with Application to Clustering with Side-Information, Eric R Xing ... 1155 Topographic Map Formation by Silicon Growth Cones, Brian Taba and Kwabena Boahen 1163 Spike Timing-Dependent Plasticity in the ...
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Advances in Neural Information …, 1996
The Support Vector (SV) method was recently proposed for estimating regressions, constructing mul... more 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 report results of applying the SV method to these problems. 1 Introduction The ...
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In this report we describe how the Support Vector (SV) technique of solving linear operator equat... more In this report we describe how the Support Vector (SV) technique of solving linear operator equations can be applied to the problem of density estimation 4]. We present a new optimization procedure and set of kernels closely related to current SV techniques that guarantee the monotonicity of the approximation. This technique estimates densities with a mixture of bumps (Gaussian-like shapes), with the usual SV property that only some coe cients are non-zero. Both the width and the height of each bump is chosen adaptively, by ...
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. Support Vector Machines are used for time series predictionand compared to radial basis functio... more . Support Vector Machines are used for time series predictionand compared to radial basis function networks. We make use of twodierent cost functions for Support Vectors: training with (i) an insensitiveloss and (ii) Huber's robust loss function and discuss how to choosethe regularization parameters in these models. Two applications are considered:data from (a) a noisy (normal and uniform noise) Mackey
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… Conference on Artificial …, 1995
Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. M uller... more Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. M uller, E. S ackinger, P. Simard, and V. Vapnik Bell Laboratories, Holmdel, NJ 07733, USA Email: yann@research.att.com ... Abstract This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition time, and memory requirements. ... COMPARISON OF LEARNING ALGORITHMS FOR HANDWRITTEN DIGIT RECOGNITION
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this report we describe how the Support Vector (SV) technique of solving linearoperator equations... more this report we describe how the Support Vector (SV) technique of solving linearoperator equations can be applied to the problem of density estimation [4]. Wepresent a new optimization procedure and set of kernels closely related to current SVtechniques that guarantee the monotonicity of the approximation. This techniqueestimates densities with a mixture of bumps (Gaussian-like shapes), with the usualSV property that only some coefficients are non-zero. Both the width and theheight of each bump is chosen adaptively ...
Bookmarks Related papers MentionsView impact
IEEE Transactions on Signal Processing, 1997
Bookmarks Related papers MentionsView impact
IEEE Transactions on Neural Networks, 1997
Page 1. Statistics for Engineering and Information Science Vladimir N. Vapnik he Nature f Statist... more Page 1. Statistics for Engineering and Information Science Vladimir N. Vapnik he Nature f Statistical earning Theo econd Edition Springer Page 2. Page 3. ... Page 5. Vladimir N. Vapnik The Nature of Statistical Learning Theory Second Edition With 50 Illustrations Springer Page 6. ...
Bookmarks Related papers MentionsView impact
Computers & Mathematics with Applications, 2001
... Wu, KY Michael Wong and David Bodoff 513 Distance Metric Learning with Application to Cluster... more ... Wu, KY Michael Wong and David Bodoff 513 Distance Metric Learning with Application to Clustering with Side-Information, Eric R Xing ... 1155 Topographic Map Formation by Silicon Growth Cones, Brian Taba and Kwabena Boahen 1163 Spike Timing-Dependent Plasticity in the ...
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Advances in Neural Information …, 1996
The Support Vector (SV) method was recently proposed for estimating regressions, constructing mul... more 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 report results of applying the SV method to these problems. 1 Introduction The ...
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In this report we describe how the Support Vector (SV) technique of solving linear operator equat... more In this report we describe how the Support Vector (SV) technique of solving linear operator equations can be applied to the problem of density estimation 4]. We present a new optimization procedure and set of kernels closely related to current SV techniques that guarantee the monotonicity of the approximation. This technique estimates densities with a mixture of bumps (Gaussian-like shapes), with the usual SV property that only some coe cients are non-zero. Both the width and the height of each bump is chosen adaptively, by ...
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact
. Support Vector Machines are used for time series predictionand compared to radial basis functio... more . Support Vector Machines are used for time series predictionand compared to radial basis function networks. We make use of twodierent cost functions for Support Vectors: training with (i) an insensitiveloss and (ii) Huber's robust loss function and discuss how to choosethe regularization parameters in these models. Two applications are considered:data from (a) a noisy (normal and uniform noise) Mackey
Bookmarks Related papers MentionsView impact
… Conference on Artificial …, 1995
Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. M uller... more Y. LeCun, L. Jackel, L. Bottou, A. Brunot, C. Cortes, J. Denker, H. Drucker, I. Guyon, U. M uller, E. S ackinger, P. Simard, and V. Vapnik Bell Laboratories, Holmdel, NJ 07733, USA Email: yann@research.att.com ... Abstract This paper compares the performance of several classifier algorithms on a standard database of handwritten digits. We consider not only raw accuracy, but also rejection, training time, recognition time, and memory requirements. ... COMPARISON OF LEARNING ALGORITHMS FOR HANDWRITTEN DIGIT RECOGNITION
Bookmarks Related papers MentionsView impact
Bookmarks Related papers MentionsView impact