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Distributed Support Vector Machines
IEEE Transactions on Neural Networks, 2006A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes).
A, Navia-Vazquez +3 more
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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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Arbitrary Norm Support Vector Machines
Neural Computation, 2009Support vector machines (SVM) are state-of-the-art classifiers. Typically L2-norm or L1-norm is adopted as a regularization term in SVMs, while other norm-based SVMs, for example, the L0-norm SVM or even the L∞-norm SVM, are rarely seen in the literature.
Huang, Kaizhu +3 more
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2013
Fisher’s linear discriminant function (LDF) and related classifiers for binary and multiclass learning problems have performed well for many years and for many data sets. Recently, a brand-new learning methodology, support vector machines (SVMs), has emerged (Boser, Guyon, and Vapnik, 1992), which has matched the performance of the LDF and, in many ...
Gareth James +3 more
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Fisher’s linear discriminant function (LDF) and related classifiers for binary and multiclass learning problems have performed well for many years and for many data sets. Recently, a brand-new learning methodology, support vector machines (SVMs), has emerged (Boser, Guyon, and Vapnik, 1992), which has matched the performance of the LDF and, in many ...
Gareth James +3 more
+4 more sources
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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Functional support vector machine
BiostatisticsAbstract Linear and generalized linear scalar-on-function modeling have been commonly used to understand the relationship between a scalar response variable (e.g. continuous, binary outcomes) and functional predictors. Such techniques are sensitive to model misspecification when the relationship between the response variable and the ...
Shanghong, Xie, R Todd, Ogden
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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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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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2011
A support vector machine (SVM) searches for so-called support vectors which are observations that are found to lie at the edge of an area in space which presents a boundary between one of these classes of observations (e.g., the squares) and another class of observations (e.g., the circles).
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A support vector machine (SVM) searches for so-called support vectors which are observations that are found to lie at the edge of an area in space which presents a boundary between one of these classes of observations (e.g., the squares) and another class of observations (e.g., the circles).
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2019
This chapter covers another major and powerful machine learning tool which is SVM. The chapter begins with the introduction of linear classifier, K-NN classifier, and perceptron which are the key to understand discriminative approaches such as SVM and ANN. After these preparations, the primal form SVM is formally introduced.
Jana Aberham, Fabrizio Kuruc
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This chapter covers another major and powerful machine learning tool which is SVM. The chapter begins with the introduction of linear classifier, K-NN classifier, and perceptron which are the key to understand discriminative approaches such as SVM and ANN. After these preparations, the primal form SVM is formally introduced.
Jana Aberham, Fabrizio Kuruc
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