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251
An iterative algorithm learning the maximal margin classifier
, 2003
"... A simple learning algorithm for maximal margin classi ers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger–Kozinec algorithm (S–Kalgorithm) from 1981 which nds a maximal margin hyperplane with a given precision for s ..."
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Cited by 7 (0 self)
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A simple learning algorithm for maximal margin classi ers (also support vector machines with quadratic cost function) is proposed. We build our iterative algorithm on top of the Schlesinger–Kozinec algorithm (S–Kalgorithm) from 1981 which nds a maximal margin hyperplane with a given precision
An Application of a Random Sampling Technique to PrimalForm MaximalMargin Classifiers
"... Random sampling techniques have been developed in for geometric/combinatorial optimization problems; see, e.g., [Cla88, Cla95, AS93, GW99]. In this note, we apply one of these techniques for obtaining (hopefully) efficient support vector machine training algorithm. In particular, we propose one way ..."
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Random sampling techniques have been developed in for geometric/combinatorial optimization problems; see, e.g., [Cla88, Cla95, AS93, GW99]. In this note, we apply one of these techniques for obtaining (hopefully) efficient support vector machine training algorithm. In particular, we propose one way to find "outliers" by using the sampling technique.
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 521 (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
SVM: Terminology 4(6) Error or
"... The maximal margin classifier is similar to the perceptron: • It also assumes that the data points are linearly separable • It aims at finding the separating hyperplane with the maximal geometric margin (not just anyone typical of a perceptron) x 2 ..."
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The maximal margin classifier is similar to the perceptron: • It also assumes that the data points are linearly separable • It aims at finding the separating hyperplane with the maximal geometric margin (not just anyone typical of a perceptron) x 2
A training algorithm for optimal margin classifiers
 PROCEEDINGS OF THE 5TH ANNUAL ACM WORKSHOP ON COMPUTATIONAL LEARNING THEORY
, 1992
"... A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters is adjust ..."
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Cited by 1865 (43 self)
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A training algorithm that maximizes the margin between the training patterns and the decision boundary is presented. The technique is applicable to a wide variety of classifiaction functions, including Perceptrons, polynomials, and Radial Basis Functions. The effective number of parameters
Machine Learning, 37(3):277296, 1999. Large Margin Classification Using the Perceptron Algorithm
"... Abstract. 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 margin ..."
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Abstract. 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
Large Margin Classification Using the Perceptron Algorithm Machine Learning, 37(3):277296, 1999.
"... Abstract. 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 margin ..."
Abstract
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Abstract. 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
Large Margin Classification Using the Perceptron Algorithm Machine Learning, 37(3):277296, 1999.
"... Abstract. 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 margin ..."
Abstract
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Abstract. 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
Bayes Optimal Hyperplanes → Maximal Margin Hyperplanes
 IJCAI'99 WORKSHOP ON SUPPORT VECTOR MACHINES (ROBOTICS.STANFORD.EDU/~KOLLER
, 1999
"... Maximal margin classifiers are a core technology in modern machine learning. They have strong theoretical justifications and have shown empirical successes. We provide an alternative justification for maximal margin hyperplane classifiers by relating them to Bayes optimal classifiers that use P ..."
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Cited by 3 (0 self)
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Maximal margin classifiers are a core technology in modern machine learning. They have strong theoretical justifications and have shown empirical successes. We provide an alternative justification for maximal margin hyperplane classifiers by relating them to Bayes optimal classifiers that use
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 604 (15 self)
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In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from
Results 1  10
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251