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Large Margin Classification Using the Perceptron Algorithm
 Machine Learning
, 1998
"... We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable with large ..."
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Cited by 518 (2 self)
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We introduce and analyze a new algorithm for linear classification which combines Rosenblatt 's perceptron algorithm with Helmbold and Warmuth's leaveoneout method. Like Vapnik 's maximalmargin classifier, our algorithm takes advantage of data that are linearly separable
Large Margin Classification for Moving Targets
 IN PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON ALGORITHMIC LEARNING THEORY
, 2002
"... We consider using online large margin classification algorithms in a setting where the target classifier may change over time. The algorithms we consider are Gentile's Alma, and an algorithm we call Norma which performs a modified online gradient descent with respect to a regularised risk. The ..."
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Cited by 1 (1 self)
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We consider using online large margin classification algorithms in a setting where the target classifier may change over time. The algorithms we consider are Gentile's Alma, and an algorithm we call Norma which performs a modified online gradient descent with respect to a regularised risk
VARIABILITY REGULARIZATION IN LARGEMARGIN CLASSIFICATION
"... This paper introduces a novel regularization strategy to address the generalization issues for largemargin classifiers from the Empirical Risk Minimization (ERM) perspective. First, the ERM principle is argued to be more flexible than the Structural Risk Minimization (SRM) principle by reviewing t ..."
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risk minimization, model selection, model regularization, largemargin classification 1.
LargeMargin Classification in Banach Spaces
"... We propose a framework for dealing with binary hardmargin classification in Banach spaces, centering on the use of a supporting semiinnerproduct (s.i.p.) taking the place of an innerproduct in Hilbert spaces. The theory of semiinnerproduct spaces allows for a geometric, Hilbertlike formulatio ..."
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Cited by 1 (0 self)
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We propose a framework for dealing with binary hardmargin classification in Banach spaces, centering on the use of a supporting semiinnerproduct (s.i.p.) taking the place of an innerproduct in Hilbert spaces. The theory of semiinnerproduct spaces allows for a geometric, Hilbert
VARIABILITY REGULARIZATION IN LARGEMARGIN CLASSIFICATION
, 2011
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Large Margin Classification with the Progressive Hedging Algorithm
"... Several learning algorithms in classification and structured prediction are formulated as large scale optimization problems. We show that a generic iterative reformulation and resolving strategy based on the progressive hedging algorithm from stochastic programming results in a highly parallel alg ..."
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algorithm when applied to the large margin classification problem with nonlinear kernels. We also underline promising aspects of the available analysis of progressive hedging strategies. 1
Large margin classification in infinite neural networks
"... We introduce a new family of positivedefinite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed ..."
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Cited by 5 (2 self)
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We introduce a new family of positivedefinite kernels for large margin classification in support vector machines (SVMs). These kernels mimic the computation in large neural networks with one layer of hidden units. We also show how to derive new kernels, by recursive composition, that may be viewed
Coherence Functions with Applications in LargeMargin Classification Methods
, 2012
"... Support vector machines (SVMs) naturally embody sparseness due to their use of hinge loss functions. However, SVMs can not directly estimate conditional class probabilities. In this paper we propose and study a family of coherence functions, which are convex and differentiable, as surrogates of the ..."
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Cited by 1 (0 self)
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, and the limit of the minimizer of its expected error is the minimizer of the expected error of the hinge loss. We refer to the use of the coherence function in largemargin classification as “Clearning,” and we present efficient coordinate descent algorithms for the training of regularized Clearning models.
Statistical analysis of some multicategory large margin classification methods
 Journal of Machine Learning Research
, 2004
"... The purpose of this paper is to investigate statistical properties of risk minimization based multicategory classification methods. These methods can be considered as natural extensions of binary large margin classification. We establish conditions that guarantee the consistency of classifiers obtai ..."
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Cited by 72 (2 self)
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The purpose of this paper is to investigate statistical properties of risk minimization based multicategory classification methods. These methods can be considered as natural extensions of binary large margin classification. We establish conditions that guarantee the consistency of classifiers
Results 1  10
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1,982,976