Results 291 to 300 of about 1,011,171 (338)
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IEEE Transactions on Neural Networks, 2002
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions ...
Lin, Chun-Fu, Wang, Sheng-De
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A neural support vector machine
Neural Networks, 2010Support vector machines are state-of-the-art pattern recognition algorithms that are well founded in optimization and generalization theory but not obviously applicable to the brain. This paper presents Bio-SVM, a biologically feasible support vector machine.
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2023
In this chapter, we investigate Support Vector Machines (SVM) for both linearly separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract mathematical theories. We present concrete numerical examples with small datasets and provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we
Zhiyuan Wang +3 more
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In this chapter, we investigate Support Vector Machines (SVM) for both linearly separable and linearly non-separable cases, emphasizing accessibility by minimizing abstract mathematical theories. We present concrete numerical examples with small datasets and provide a step-by-step walkthrough, illustrating the inner workings of SVM. Additionally, we
Zhiyuan Wang +3 more
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American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Dirk Valkenborg +3 more
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Dirk Valkenborg +3 more
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Support Vector Machines for Dyadic Data
Neural Computation, 2006We describe a new technique for the analysis of dyadic data, where two sets of objects (row and column objects) are characterized by a matrix of numerical values that describe their mutual relationships. The new technique, called potential support vector machine (P-SVM), is a large-margin method for the construction of classifiers and regression ...
Hochreiter, Sepp, Obermayer, Klaus
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ACM SIGAPL APL Quote Quad, 2004
This paper considers the implementation of Support Vector Machines (SVM), the new extensive class of data analysis methods. SVM have a number of advantages as compared with standard data mining techniques like artificial neural networks, for example. In the paper this methodology is described in details and implemented in A+ programming language in the
Alexander Skomorokhov +1 more
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This paper considers the implementation of Support Vector Machines (SVM), the new extensive class of data analysis methods. SVM have a number of advantages as compared with standard data mining techniques like artificial neural networks, for example. In the paper this methodology is described in details and implemented in A+ programming language in the
Alexander Skomorokhov +1 more
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2020
Support vector machine is a method for classification and regression that draws an optimal boundary in the space of covariates (p dimension) when the samples \((x_1, y_1), \ldots , (x_N, y_N)\) are given. This is a method to maximize the minimum value over \(i = 1, \ldots , N\) of the distance between \(x_i\) and the boundary.
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Support vector machine is a method for classification and regression that draws an optimal boundary in the space of covariates (p dimension) when the samples \((x_1, y_1), \ldots , (x_N, y_N)\) are given. This is a method to maximize the minimum value over \(i = 1, \ldots , N\) of the distance between \(x_i\) and the boundary.
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2019
Three separate sections comprise this chapter. The first presents an overview of statistical learning theory (SLT) as applied to machine learning. The topics covered are empirical or true risk minimization, the risk minimization principle (RMP), theoretical concept of risk minimization, function f0(X) that minimizes the expected (or true) risk ...
Walker H. Land, J. David Schaffer
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Three separate sections comprise this chapter. The first presents an overview of statistical learning theory (SLT) as applied to machine learning. The topics covered are empirical or true risk minimization, the risk minimization principle (RMP), theoretical concept of risk minimization, function f0(X) that minimizes the expected (or true) risk ...
Walker H. Land, J. David Schaffer
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Support vector machines with two support vectors [PDF]
, 2009 In this article we present a new class of support vector machines for binary classification task. Our support vector machines are constructed using only two support vectors and have very low Vapnik-Chervonenkis dimension, so they generalize well. Geometrically, our approach is based on searching of a proper pair of observations from different classes ...
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2006
Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples.
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Support Vector Machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. A SVM classifiers creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes, while maximizing the distance to the nearest cleanly split examples.
openaire +2 more sources