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171,609
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 5 (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 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
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
Bayesian Network Classifiers
, 1997
"... Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less restr ..."
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Cited by 788 (23 self)
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Recent work in supervised learning has shown that a surprisingly simple Bayesian classifier with strong assumptions of independence among features, called naive Bayes, is competitive with stateoftheart classifiers such as C4.5. This fact raises the question of whether a classifier with less
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 1848 (44 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
On Discriminative vs. Generative classifiers: A comparison of logistic regression and naive Bayes
, 2001
"... We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size is i ..."
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Cited by 513 (8 self)
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We compare discriminative and generative learning as typified by logistic regression and naive Bayes. We show, contrary to a widely held belief that discriminative classifiers are almost always to be preferred, that there can often be two distinct regimes of performance as the training set size
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 560 (20 self)
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We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class
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 ..."
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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
Results 1  10
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171,609