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The Nature of Statistical Learning Theory

by Vladimir N. Vapnik , 1999
"... Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based on the deve ..."
Abstract - Cited by 13236 (32 self) - Add to MetaCart
Statistical learning theory was introduced in the late 1960’s. Until the 1990’s it was a purely theoretical analysis of the problem of function estimation from a given collection of data. In the middle of the 1990’s new types of learning algorithms (called support vector machines) based

Support Vector Machines Support Vector Machines Support Vector Machines

by J. Weston, S. Mukherjee, O. Chapelle, M. Pontil, T. Poggio, V. Vapnik, Sambarta Bhattacharjee, Minimize True Risk, Miminize Guaranteed Risk Instead , 2004
"... 2A support vector machine classifies data as ..."
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2A support vector machine classifies data as

Regularization networks and support vector machines

by Theodoros Evgeniou, Massimiliano Pontil, Tomaso Poggio - Advances in Computational Mathematics , 2000
"... Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization a ..."
Abstract - Cited by 366 (38 self) - Add to MetaCart
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization

Sparseness of support vector machines

by Ingo Steinwart , 2003
"... Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our w ..."
Abstract - Cited by 271 (35 self) - Add to MetaCart
Support vector machines (SVMs) construct decision functions that are linear combinations of kernel evaluations on the training set. The samples with non-vanishing coefficients are called support vectors. In this work we establish lower (asymptotical) bounds on the number of support vectors. On our

LIBSVM: A library for support vector machines,”

by Chih-Chung Chang , Chih-Jen Lin - ACM Transactions on Intelligent Systems and Technology, , 2011
"... Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information. ..."
Abstract - Cited by 6496 (83 self) - Add to MetaCart
Abstract LIBSVM is a library for support vector machines (SVM). Its goal is to help users to easily use SVM as a tool. In this document, we present all its implementation details. For the use of LIBSVM, the README file included in the package and the LIBSVM FAQ provide the information.

Chunking with Support Vector Machines

by Taku Kudo, Yuji Matsumoto , 2001
"... We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead ..."
Abstract - Cited by 219 (11 self) - Add to MetaCart
We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead

A tutorial on support vector machines for pattern recognition

by Christopher J. C. Burges - Data Mining and Knowledge Discovery , 1998
"... The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when SV ..."
Abstract - Cited by 3393 (12 self) - Add to MetaCart
The tutorial starts with an overview of the concepts of VC dimension and structural risk minimization. We then describe linear Support Vector Machines (SVMs) for separable and non-separable data, working through a non-trivial example in detail. We describe a mechanical analogy, and discuss when

Hidden Markov Support Vector Machines

by Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofmann , 2003
"... This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine. ..."
Abstract - Cited by 248 (9 self) - Add to MetaCart
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines and Hidden Markov Models which we call Hidden Markov Support Vector Machine.

Training Support Vector Machines: an Application to Face Detection

by Edgar Osuna, Robert Freund, Federico Girosi , 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
Abstract - Cited by 727 (1 self) - Add to MetaCart
We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision

Support vector machines for spam categorization

by Harris Drucker, Donghui Wu, Vladimir N. Vapnik - IEEE TRANSACTIONS ON NEURAL NETWORKS , 1999
"... We study the use of support vector machines (SVM’s) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number of features ..."
Abstract - Cited by 342 (2 self) - Add to MetaCart
We study the use of support vector machines (SVM’s) in classifying e-mail as spam or nonspam by comparing it to three other classification algorithms: Ripper, Rocchio, and boosting decision trees. These four algorithms were tested on two different data sets: one data set where the number
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