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Margin maximizing loss functions
 In NIPS
, 2004
"... Margin maximizing properties play an important role in the analysis of classification models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it facilitates generalization error analysis, and practically interesting because it presents a clear g ..."
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Cited by 26 (4 self)
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Margin maximizing properties play an important role in the analysis of classification models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it facilitates generalization error analysis, and practically interesting because it presents a clear
Margin maximizing discriminant analysis
 In Proceedings of the 15th European Conference on Machine Learning
, 2004
"... Abstract. We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels and it shoul ..."
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Cited by 10 (2 self)
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Abstract. We propose a new feature extraction method called Margin Maximizing Discriminant Analysis (MMDA) which seeks to extract features suitable for classification tasks. MMDA is based on the principle that an ideal feature should convey the maximum information about the class labels
Efficient Margin Maximizing with Boosting
, 2003
"... AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear combination. It has been observed that the generalization error of the algorithm continues to improve even after all examples are classified correctly by the current signed linear combination, whic ..."
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Cited by 50 (7 self)
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AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear combination. It has been observed that the generalization error of the algorithm continues to improve even after all examples are classified correctly by the current signed linear combination, which can be viewed as hyperplane in feature space where the base hypotheses form the features.
MARGIN: Maximal Frequent Subgraph Mining
"... The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. Maximal frequent mining has triggered much interest since the size of the set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs. We propose an algorithm that m ..."
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Cited by 23 (1 self)
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The exponential number of possible subgraphs makes the problem of frequent subgraph mining a challenge. Maximal frequent mining has triggered much interest since the size of the set of maximal frequent subgraphs is much smaller to that of the set of frequent subgraphs. We propose an algorithm
Online Relative Margin Maximization for Statistical Machine Translation
"... Recent advances in largemargin learning have shown that better generalization can be achieved by incorporating higher order information into the optimization, such as the spread of the data. However, these solutions are impractical in complex structured prediction problems such as statistical machi ..."
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Cited by 2 (0 self)
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machine translation. We present an online gradientbased algorithm for relative margin maximization, which bounds the spread of the projected data while maximizing the margin. We evaluate our optimizer on ChineseEnglish and ArabicEnglish translation tasks, each with small and large feature sets
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
Entropy and margin maximization for structured output learning
 In Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III, ECML PKDD’10
, 2010
"... Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs). A generalized loss function is introduced, which jointly maximizes the entropy and the margin of the solution. The CRF ..."
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Cited by 9 (1 self)
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Abstract. We consider the problem of training discriminative structured output predictors, such as conditional random fields (CRFs) and structured support vector machines (SSVMs). A generalized loss function is introduced, which jointly maximizes the entropy and the margin of the solution
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
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
Results 1  10
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