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Sequential Minimal Optimization for SVM
"... This is a C++ implementation of John C. Platt's sequential minimal optimization (SMO) for training a support vector machine (SVM). This program is based on the pseudocode in Platt (1998). This is both the documentation and the C++ code. It is a NUWEB document from which both the L A T E X le ..."
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This is a C++ implementation of John C. Platt's sequential minimal optimization (SMO) for training a support vector machine (SVM). This program is based on the pseudocode in Platt (1998). This is both the documentation and the C++ code. It is a NUWEB document from which both the L A T E X le
Sequential minimal optimization: A fast algorithm for training support vector machines
 Advances in Kernel MethodsSupport Vector Learning
, 1999
"... This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest possi ..."
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Cited by 451 (3 self)
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This paper proposes a new algorithm for training support vector machines: Sequential Minimal Optimization, or SMO. Training a support vector machine requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this large QP problem into a series of smallest
SEQUENTIAL MINIMAL OPTIMIZATION IN SUPPORT VECTOR MACHINE
"... Computer based medical decision support system (MDSS) can be useful for the physicians with its fast and accurate decision making process. Predicting the existence of heart disease accurately, results in saving life of patients followed by proper treatment. The main objective of our paper is to pres ..."
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is to present a MDSS for heart disease classification based on sequential minimal optimization (SMO) technique in support vector machine (SVM). In this we illustrated the UCI machine learning repository data of Cleveland heart disease database; we trained SVM by using SMO technique. Training a SVM requires
Fast kernel learning using sequential minimal optimization
, 2004
"... While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimiz ..."
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Cited by 9 (0 self)
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; moreover, the sequential minimal optimization (SMO) techniques that are essential in largescale implementations of the SVM cannot be applied because the cost function is nondifferentiable. We propose a novel dual formulation of the QCQP as a secondorder cone programming problem, and show how to exploit
Fast Kernel Learning Using Sequential Minimal Optimization
, 2004
"... While classical kernelbased classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the opti ..."
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; moreover, the sequential minimal optimization (SMO) techniques that are essential in largescale implementations of the SVM cannot be applied because the cost function is nondi#erentiable. We propose a novel dual formulation of the QCQP as a secondorder cone programming problem, and show how
Sequential Minimal Optimization in AdaptiveBandwidth Convex Clustering
"... Computing not the local, but the global optimum of a cluster assignment is one of the important aspects in clustering. Convex clustering is an approach to acquire the global optimum, assuming some fixed centers and bandwidths of the clusters. The essence of the convex clustering is a convex optimiza ..."
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Cited by 2 (1 self)
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optimization of the mixture weights whose optimum becomes sparse. One of the limitations in the convex clustering was the computational inefficiency of the ExpectationMaximization algorithm, where an extremely large number of iterations is required for the convergence. This paper proposes a more efficient
Support Vector Machines for Regression Problems with Sequential Minimal Optimization
, 1999
"... Training a support vector machine (SVM) is usually done by mapping the underlying optimization problem into a quadratic programming (QP) problem. Unfortunately, high quality QP solvers are not readily available, which makes research into the area of SVMs difficult for the those without a QP solver. ..."
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Cited by 1 (1 self)
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. Recently, the Sequential Minimal Optimization algorithm (SMO) was introduced [1, 2]. SMO reduces SVM training down to a series of smaller QP subproblems that have an analytical solution and, therefore, does not require a general QP solver. SMO has been shown to be very efficient for classification problems
824 New vSupport Vector Machines and their Sequential Minimal Optimization
"... Although the vSupport Vector Machine, vSVM, (SchSlkopf et al., 2000) has the advantage of using a single parameter v to control both the number of support vectors and the fraction of margin errors, there are two issues that prevent it from being used in many real world applications. First, unlike ..."
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training more difficult. Sequential Minimal Optimization (SMO) algorithms that are very easy to implement and scalable to very large problems do not exist in a good form for vSVM. In this paper, we proposed two new vSVM formulations. These formulations introduce means to control the misclassification
MaxMargin Learning of Gaussian Mixtures with Sequential Minimal Optimization
"... This works deals with discriminant training of Gaussian Mixture Models through margin maximization. We go one step further previous work, we propose a new formulation of the learning problem that allows the use of efficient optimization algorithm popularized for Support Vector Machines, yielding imp ..."
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This works deals with discriminant training of Gaussian Mixture Models through margin maximization. We go one step further previous work, we propose a new formulation of the learning problem that allows the use of efficient optimization algorithm popularized for Support Vector Machines, yielding
On the Equality of Kernel AdaTron and Sequential Minimal Optimization in Classification and Regression Tasks and Alike Algorithms for Kernel
 Machines, Proc. of ESANN 2003, 11 th European Symposium on Artificial Neural Networks
"... Abstract: The paper presents the equality of a kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and sequential minimal optimization (SMO) learning algorithm (based on an analytic quadratic programming step) in designing the support vector machines (SVMs) having posit ..."
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Cited by 9 (4 self)
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Abstract: The paper presents the equality of a kernel AdaTron (KA) method (originating from a gradient ascent learning approach) and sequential minimal optimization (SMO) learning algorithm (based on an analytic quadratic programming step) in designing the support vector machines (SVMs) having
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