Results 1  10
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267
Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods
 ADVANCES IN LARGE MARGIN CLASSIFIERS
, 1999
"... The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score. Howev ..."
Abstract

Cited by 1051 (0 self)
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The output of a classifier should be a calibrated posterior probability to enable postprocessing. Standard SVMs do not provide such probabilities. One method to create probabilities is to directly train a kernel classifier with a logit link function and a regularized maximum likelihood score
Feature selection for SVMs
 Advances in Neural Information Processing Systems 13
, 2000
"... We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leaveoneout error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard ..."
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Cited by 282 (17 self)
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We introduce a method of feature selection for Support Vector Machines. The method is based upon finding those features which minimize bounds on the leaveoneout error. This search can be efficiently performed via gradient descent. The resulting algorithms are shown to be superior to some standard
Learning realistic human actions from movies
 IN: CVPR.
, 2008
"... The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribut ..."
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Cited by 738 (48 self)
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next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local spacetime features, spacetime pyramids and multichannel nonlinear SVMs. The method is shown to improve state
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
Abstract

Cited by 476 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 461 (3 self)
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is avoided, SMO scales somewhere between linear and quadratic in the training set size for various test problems, while the standard chunking SVM algorithm scales somewhere between linear and cubic in the training set size. SMO’s computation time is dominated by SVM evaluation, hence SMO is fastest
OneClass SVMs for Document Classification
 Journal of Machine Learning Research
, 2001
"... We implemented versions of the SVM appropriate for oneclass classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of oneclass SV ..."
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Cited by 185 (3 self)
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We implemented versions of the SVM appropriate for oneclass classification in the context of information retrieval. The experiments were conducted on the standard Reuters data set. For the SVM implementation we used both a version of Schölkopf et al. and a somewhat different version of one
Speech Recognition using SVMs
 Advances in Neural Information Processing Systems 14
, 2002
"... An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihoodratio. The sco ..."
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Cited by 82 (17 self)
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An important issue in applying SVMs to speech recognition is the ability to classify variable length sequences. This paper presents extensions to a standard scheme for handling this variable length data, the Fisher score. A more useful mapping is introduced based on the likelihood
Large scale multiple kernel learning
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... While classical kernelbased learning algorithms are based on a single kernel, in practice it is often desirable to use multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for classification, leading to a convex quadratically constrained quadratic program. We s ..."
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Cited by 340 (20 self)
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show that it can be rewritten as a semiinfinite linear program that can be efficiently solved by recycling the standard SVM implementations. Moreover, we generalize the formulation and our method to a larger class of problems, including regression and oneclass classification. Experimental results
Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales
 In Proc. 43st ACL
, 2005
"... We address the ratinginference problem, wherein rather than simply decide whether a review is “thumbs up ” or “thumbs down”, as in previous sentiment analysis work, one must determine an author’s evaluation with respect to a multipoint scale (e.g., one to five “stars”). This task represents an int ..."
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Cited by 298 (2 self)
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an interesting twist on standard multiclass text categorization because there are several different degrees of similarity between class labels; for example, “three stars ” is intuitively closer to “four stars ” than to “one star”. We first evaluate human performance at the task. Then, we apply a metaalgorithm
Adaptive Scaling for Feature Selection in SVMs
, 2002
"... This paper introduces an algorithm for the automatic relevance determination of input variables in kernelized Support Vector Machines. Relevance is measured by scale factors defining the input space metric, and feature selection is performed by assigning zero weights to irrelevant variables. The met ..."
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Cited by 53 (1 self)
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. The metric is automatically tuned by the minimization of the standard SVM empirical risk, where scale factors are added to the usual set of parameters defining the classifier. Feature selection is achieved by constraints encouraging the sparsity of scale factors. The resulting algorithm compares favorably
Results 1  10
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267