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32
A tutorial on support vector regression
, 2004
"... In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing ..."
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Cited by 473 (2 self)
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In this tutorial we give an overview of the basic ideas underlying Support Vector (SV) machines for function estimation. Furthermore, we include a summary of currently used algorithms for training SV machines, covering both the quadratic (or convex) programming part and advanced methods for dealing with large datasets. Finally, we mention some modifications and extensions that have been applied to the standard SV algorithm, and discuss the aspect of regularization from a SV perspective.
Robust Linear Programming Discrimination Of Two Linearly Inseparable Sets
, 1992
"... INTRODUCTION We consider the two pointsets A and B in the ndimensional real space R n represented by the m \Theta n matrix A and the k \Theta n matrix B respectively. Our principal objective here is to formulate a single linear program with the following properties: (i) If the convex hulls of A ..."
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Cited by 211 (33 self)
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INTRODUCTION We consider the two pointsets A and B in the ndimensional real space R n represented by the m \Theta n matrix A and the k \Theta n matrix B respectively. Our principal objective here is to formulate a single linear program with the following properties: (i) If the convex hulls of A and B are disjoint, a strictly separating plane is obtained. (ii) If the convex hulls of A and B intersect, a plane is obtained that minimizes some measure of misclassification points, for all possible cases. (iii) No extraneous constraints are imposed on the linear program that rule out any specific case from consideration. Most linear programming formulations 6,5,12,4 have property (i)
A New Approximate Maximal Margin Classification Algorithm
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2001
"... A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alma p (Approximate Large Margin algorithm w.r.t. norm p), takes O (p 1) 2 2 corrections to separate the data wi ..."
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Cited by 87 (6 self)
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A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p 2 for a set of linearly separable data. Our algorithm, called alma p (Approximate Large Margin algorithm w.r.t. norm p), takes O (p 1) 2 2 corrections to separate the data with pnorm margin larger than (1 ) , where is the (normalized) pnorm margin of the data. alma p avoids quadratic (or higherorder) programming methods. It is very easy to implement and is as fast as online algorithms, such as Rosenblatt's Perceptron algorithm. We performed extensive experiments on both realworld and artificial datasets. We compared alma 2 (i.e., alma p with p = 2) to standard Support vector Machines (SVM) and to two incremental algorithms: the Perceptron algorithm and Li and Long's ROMMA. The accuracy levels achieved by alma 2 are superior to those achieved by the Perceptron algorithm and ROMMA, but slightly inferior to SVM's. On the other hand, alma 2 is quite faster and easier to implement than standard SVM training algorithms. When learning sparse target vectors, alma p with p > 2 largely outperforms Perceptronlike algorithms, such as alma 2 .
NeuralNetwork Feature Selector
 IEEE Transactions on Neural Networks
, 1997
"... Feature selection is an integral part of most learning algorithms. Due to the existence of irrelevant and redundant attributes, by selecting only the relevant attributes of the data, higher predictive accuracy can be expected from a machine learning method. In this paper, we propose the use of a ..."
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Cited by 56 (3 self)
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Feature selection is an integral part of most learning algorithms. Due to the existence of irrelevant and redundant attributes, by selecting only the relevant attributes of the data, higher predictive accuracy can be expected from a machine learning method. In this paper, we propose the use of a threelayer feedforward neural network to select those input attributes that are most useful for discriminating classes in a given set of input patterns. A network pruning algorithm is the foundation of the proposed algorithm. By adding a penalty term to the error function of the network, redundant network connections can be distinguished from those relevant ones by their small weights when the network training process has been completed. A simple criterion to remove an attribute based on the accuracy rate of the network is developed. The network is retrained after removal of an attribute, and the selection process is repeated until no attribute meets the criterion for removal. Our ...
Multicategory Classification by Support Vector Machines
 Computational Optimizations and Applications
, 1999
"... We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how twoclass discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programm ..."
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Cited by 56 (0 self)
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We examine the problem of how to discriminate between objects of three or more classes. Specifically, we investigate how twoclass discrimination methods can be extended to the multiclass case. We show how the linear programming (LP) approaches based on the work of Mangasarian and quadratic programming (QP) approaches based on Vapnik's Support Vector Machines (SVM) can be combined to yield two new approaches to the multiclass problem. In LP multiclass discrimination, a single linear program is used to construct a piecewise linear classification function. In our proposed multiclass SVM method, a single quadratic program is used to construct a piecewise nonlinear classification function. Each piece of this function can take the form of a polynomial, radial basis function, or even a neural network. For the k > 2 class problems, the SVM method as originally proposed required the construction of a twoclass SVM to separate each class from the remaining classes. Similarily, k twoclass linear programs can be used for the multiclass problem. We performed an empirical study of the original LP method, the proposed k LP method, the proposed single QP method and the original k QP methods. We discuss the advantages and disadvantages of each approach. 1 1
A Clustering Technique for the Identification of Piecewise Affine Systems
, 2001
"... We propose a new technique for the identification of discretetime hybrid systems in the PieceWise Affine (PWA) form. This problem can be formulated as the reconstruction of a possibly discontinuous PWA map with a multidimensional domain. In order to achieve our goal, we provide an algorithm that ..."
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Cited by 49 (7 self)
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We propose a new technique for the identification of discretetime hybrid systems in the PieceWise Affine (PWA) form. This problem can be formulated as the reconstruction of a possibly discontinuous PWA map with a multidimensional domain. In order to achieve our goal, we provide an algorithm that exploits the combined use of clustering, linear identification, and pattern recognition techniques. This allows to identify both the affine submodels and the polyhedral partition of the domain on which each submodel is valid avoiding gridding procedures. Moreover, the clustering step (used for classifying the datapoints) is performed in a suitably defined feature space which allows also to reconstruct different submodels that share the same coefficients but are defined on different regions. Measures of confidence on the samples are introduced and exploited in order to improve the performance of both the clustering and the final linear regression procedure.
Mathematical Programming for Data Mining: Formulations and Challenges
 INFORMS Journal on Computing
, 1998
"... This paper is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research ch ..."
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Cited by 47 (0 self)
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This paper is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems within the area of knowledge discovery in databases, our aim is to list some of the pressing research challenges, and outline opportunities for contributions by the optimization research communities. Towards these goals, we include formulations of the basic categories of data mining methods as optimization problems. We also provide examples of successful mathematical programming approaches to some data mining problems. keywords: data analysis, data mining, mathematical programming methods, challenges for massive data sets, classification, clustering, prediction, optimization. To appear: INFORMS: Journal of Compting, special issue on Data Mining, A. Basu and B. Golden (guest editors). Also appears as Mathematical Programming Technical Report 9801, Computer Sciences Department, University of Wi...
Mathematical Programming in Neural Networks
 ORSA Journal on Computing
, 1993
"... This paper highlights the role of mathematical programming, particularly linear programming, in training neural networks. A neural network description is given in terms of separating planes in the input space that suggests the use of linear programming for determining these planes. A more standard d ..."
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Cited by 40 (13 self)
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This paper highlights the role of mathematical programming, particularly linear programming, in training neural networks. A neural network description is given in terms of separating planes in the input space that suggests the use of linear programming for determining these planes. A more standard description in terms of a mean square error in the output space is also given, which leads to the use of unconstrained minimization techniques for training a neural network. The linear programming approach is demonstrated by a brief description of a system for breast cancer diagnosis that has been in use for the last four years at a major medical facility. 1 What is a Neural Network? A neural network is a representation of a map between an input space and an output space. A principal aim of such a map is to discriminate between the elements of a finite number of disjoint sets in the input space. Typically one wishes to discriminate between the elements of two disjoint point sets in the ndim...
ArbitraryNorm Separating Plane
 Operations Research Letters
, 1997
"... A plane separating two point sets in ndimensional real space is constructed such that it minimizes the sum of arbitrarynorm distances of misclassified points to the plane. In contrast to previous approaches that used surrogates for distanceminimization, the present work is based on a precise norm ..."
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Cited by 38 (13 self)
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A plane separating two point sets in ndimensional real space is constructed such that it minimizes the sum of arbitrarynorm distances of misclassified points to the plane. In contrast to previous approaches that used surrogates for distanceminimization, the present work is based on a precise normdependent explicit closed form for the projection of a point on a plane. This projection is used to formulate the separatingplane problem as a minimization of a convex function on a unit sphere in a norm dual to that of the arbitrary norm used. For the 1norm, the problem can be solved in polynomial time by solving 2n linear programs or by solving a bilinear program. For a general pnorm, the minimization problem can be transformed via an exact penalty formulation to minimizing the sum of a convex function and a bilinear function on a convex set. For the one and infinity norms, a finite successive linearization algorithm can be used for solving the exact penalty formulation. 1 Introduction...