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SupportVector Networks
 Machine Learning
, 1995
"... The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
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Cited by 3621 (35 self)
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The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special
A neural support vector network architecture with adaptive kernels
 In Proc. of the International Joint Conference on Neural Networks
, 2000
"... In the Support Vector Machines (SVM) framework, the positivedefinite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant function is obtained by taking a linear combination of the kernels computed at training examples called support vectors. Here w ..."
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Cited by 8 (2 self)
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training neural networks for classification tasks. Several experiments are performed which show that such Neural Support Vector Networks perform similarly to SVMs while requiring significantly fewer support vectors, even when the similarity measure has no internal parameters. 1
A Neural Support Vector Network Architecture with Adaptive Kernels
 In Proc. of the International Joint Conference on Neural Networks
, 2000
"... In the Support Vector Machines (SVM) framework, the positivedefinite kernel can be seen as representing a fixed similarity measure between two patterns, and a discriminant function is obtained by taking a linear combination of the kernels computed at training examples called support vectors. Here ..."
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when training neural networks for classification tasks. Several experiments are performed which show that such Neural Support Vector Networks perform similarly to SVMs while requiring significantly fewer support vectors, even when the similarity measure has no internal parameters. 1
Support Vector Networks for Prediction of Floor Pressures in Shallow Cavity Flows
"... (SVM) have proved to be very successful tools for classification and regression problems. The representational performance of this type of networks is studied on a cavity flow facility developed to investigate the characteristics of aerodynamic flows at various Mach numbers. Several test conditions ..."
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(SVM) have proved to be very successful tools for classification and regression problems. The representational performance of this type of networks is studied on a cavity flow facility developed to investigate the characteristics of aerodynamic flows at various Mach numbers. Several test conditions
Supportvector machines for histogrambased image classification
 IEEE Transactions on Neural Networks
, 1999
"... Abstract — Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM’s) can generalize well on difficult image classification problems where the only features are ..."
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Cited by 221 (1 self)
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Abstract — Traditional classification approaches generalize poorly on image classification tasks, because of the high dimensionality of the feature space. This paper shows that support vector machines (SVM’s) can generalize well on difficult image classification problems where the only features
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 728 (1 self)
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We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision
A practical guide to support vector classification
, 2010
"... The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results. ..."
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Cited by 787 (7 self)
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The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results.
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 ..."
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Cited by 446 (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
Regularization networks and support vector machines
 Advances in Computational Mathematics
, 2000
"... Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization a ..."
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Cited by 366 (38 self)
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Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization
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 594 (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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