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387
Unified dual for biclass SVM approaches
"... SVM theory was originally developed on the basis of a separable binary classification problem, and other approaches have been later introduced. In this paper is demonstrated that all these approaches admit the same dual problem formulation. Let Z = ((x1, y1),..., (xn, yn)) = (z1,..., zn) ∈ (X × Y) ..."
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Cited by 3 (0 self)
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SVM theory was originally developed on the basis of a separable binary classification problem, and other approaches have been later introduced. In this paper is demonstrated that all these approaches admit the same dual problem formulation. Let Z = ((x1, y1),..., (xn, yn)) = (z1,..., zn) ∈ (X × Y
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2000
"... We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class ..."
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Cited by 561 (20 self)
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We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a marginbased binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class
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 476 (46 self)
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of equations in the dual space. While the SVM classifier has a large margin interpretation, the LSSVM formulation is related in this paper to a ridge regression approach for classification with binary targets and to Fisher's linear discriminant analysis in the feature space. Multiclass categorization
Dual unification of biclass Support Vector Machine formulations
"... Support Vector Machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the ..."
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Support Vector Machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation
A unified approach to approximating resource allocation and scheduling
 Journal of the ACM
, 2000
"... We present a general framework for solving resource allocation and scheduling problems. Given a resource of fixed size, we present algorithms that approximate the maximum throughput or the minimum loss by a constant factor. Our approximation factors apply to many problems, among which are: (i) real ..."
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Cited by 156 (23 self)
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approximation algorithm. Our algorithms are simple and efficient and are based on the localratio technique. We note that they can equivalently be interpreted within the primaldual schema.
A Unified SVM Framework for Signal Estimation
"... This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has g ..."
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This paper presents a review in the form of a unified framework for tackling estimation problems in Digital Signal Processing (DSP) using Support Vector Machines (SVMs). The paper formalizes our developments in the area of DSP with SVM principles. The use of SVMs for DSP is already mature, and has
A large N duality via a geometric transition
 Nucl. Phys. B
, 2001
"... We propose a large N dual of 4d, N = 1 supersymmetric, SU(N) YangMills with adjoint field Φ and arbitrary superpotential W(Φ). The field theory is geometrically engineered via Dbranes partially wrapped over certain cycles of a nontrivial CalabiYau geometry. The large N, or lowenergy, dual arise ..."
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Cited by 187 (27 self)
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techniques for analyzing the strongly coupled, supersymmetric gauge theories. Moreover, the proposed large N dual provides a simpler and more unified approach for obtaining exact results for this class of supersymmetric gauge theories. March
Fast Global Minimization of the Active Contour/Snake Model
"... The active contour/snake model is one of the most successful variational models in image segmentation. It consists of evolving a contour in images toward the boundaries of objects. Its success is based on strong mathematical properties and efficient numerical schemes based on the level set method. ..."
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Cited by 161 (10 self)
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. The only drawback of this model is the existence of local minima in the active contour energy, which makes the initial guess critical to get satisfactory results. In this paper, we propose to solve this problem by determining a global minimum of the active contour model. Our approach is based
Graph Matching With a DualStep EM Algorithm
 IEEE Transactions on Pattern Analysis and Machine Intelligence
, 1998
"... Abstract—This paper describes a new approach to matching geometric structure in 2D pointsets. The novel feature is to unify the tasks of estimating transformation geometry and identifying pointcorrespondence matches. Unification is realized by constructing a mixture model over the bipartite graph ..."
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Cited by 104 (6 self)
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Abstract—This paper describes a new approach to matching geometric structure in 2D pointsets. The novel feature is to unify the tasks of estimating transformation geometry and identifying pointcorrespondence matches. Unification is realized by constructing a mixture model over the bipartite graph
Advanced Adaptive Critic Designs
 in Proc. World Congress on Neural Networks
, 1996
"... We discuss a variety of Adaptive Critic Designs (ACDs) for neurocontrol. These are suitable for learning in noisy, nonlinear, and nonstationary environments. They have common roots as generalizations of dynamic programming for neural reinforcement learning approaches. Our discussion of these origins ..."
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Cited by 134 (13 self)
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that are currently the only working implementations of GDHP. They promise to be useful for many engineering applications in the areas of optimization and optimal control. Based on one of these modifications, we present a unified approach to all ACDs. This leads to a generalized training procedure for ACDs.
Results 1  10
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387