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1,863
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 727 (1 self)
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global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of subproblems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping
SPADE: An efficient algorithm for mining frequent sequences
 Machine Learning
, 2001
"... Abstract. In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original proble ..."
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Cited by 437 (16 self)
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Abstract. In this paper we present SPADE, a new algorithm for fast discovery of Sequential Patterns. The existing solutions to this problem make repeated database scans, and use complex hash structures which have poor locality. SPADE utilizes combinatorial properties to decompose the original
Bounding the Suboptimality of Reusing Subproblems
, 1998
"... We are interested in the problem of determining a course of action to achieve a desired objective in a nondeterministic environment. Markov decision processes (MDPs) provide a framework for representing this action selection problem, and there are a number of algorithms that learn optimal policies w ..."
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Cited by 13 (5 self)
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to subproblems. This is done within a restricted class of MDPs, namely those where nonzero reward is received only upon reaching a goal state. We introduce the definition of a subproblem within this class and provide motivation for how reuse of subproblem solutions can speed up learning. The contribution
Bounding the Suboptimality of Reusing Subproblems
, 1998
"... We are interested in the problem of determining a course of action to achieve a desired objective in a nondeterministic environment. Markov decision processes (MDPs) provide a framework for representing this action selection problem, and there are a number of algorithms that learn optimal poli ..."
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that are solutions to subproblems. This is done within a restricted class of MDPs, namely those where nonzero reward is received only upon reaching a goal state. We introduce the definition of a subproblem within this class and provide motivation for how reuse of subproblem solutions can speed up learning
Sparse Reconstruction by Separable Approximation
, 2007
"... Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing ..."
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Cited by 373 (38 self)
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Finding sparse approximate solutions to large underdetermined linear systems of equations is a common problem in signal/image processing and statistics. Basis pursuit, the least absolute shrinkage and selection operator (LASSO), waveletbased deconvolution and reconstruction, and compressed sensing
Extracting constraint satisfaction subproblems
 In Proceedings of the 14 th International Joint Conference on Artificial Intelligence (IJCAI’95
, 1995
"... Given a subproblem, S, of a constraint satisfaction problem, we can decompose the problem into a set of disjoint subproblems one of which will be S. This decomposition permits exploitation of problemspecific metaknowledge, a priori or acquired knowledge, about S. If we know that S is unsolvable, fo ..."
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Cited by 22 (0 self)
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, for example, the decomposition permits us to extract and then discard S, restricting the search for a solution to the remaining subproblems. A variety of potential uses for the decomposition method are discussed. A specific method that dynamically discards failed subproblems during forward checking search
Inexact Solution of NLP Subproblems in MINLP
, 2011
"... In the context of convex mixedinteger nonlinear programming (MINLP), we investigate how the outer approximation method and the generalized Benders decomposition method are affected when the respective NLP subproblems are solved inexactly. We show that the cuts in the corresponding master problems c ..."
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In the context of convex mixedinteger nonlinear programming (MINLP), we investigate how the outer approximation method and the generalized Benders decomposition method are affected when the respective NLP subproblems are solved inexactly. We show that the cuts in the corresponding master problems
Inexact Solution of NLP Subproblems in MINLP
, 2012
"... In the context of convex mixed integer nonlinear programming (MINLP), we investigate how the outer approximation method and the generalized Benders decomposition method are affectedwhentherespectivenonlinearprogramming(NLP)subproblemsaresolvedinexactly. We show that the cuts in the corresponding mas ..."
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master problems can be changed to incorporate the inexact residuals, still rendering equivalence and finiteness in the limit case. Some numerical results will be presented to illustrate the behavior of the methods under NLP subproblem inexactness.
Optimality Conditions for CDT Subproblem
, 1997
"... : In this paper, we give necessary and sufficient optimality conditions which are easy verified for the local solution of CelisDennisTapia subproblem (CDT subproblem) where the Hessian at this local solution has one negative eigenvalue. If CDT subproblem has no global solution with Hessian of Lagr ..."
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Cited by 1 (0 self)
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: In this paper, we give necessary and sufficient optimality conditions which are easy verified for the local solution of CelisDennisTapia subproblem (CDT subproblem) where the Hessian at this local solution has one negative eigenvalue. If CDT subproblem has no global solution with Hessian
Machine Learning for Subproblem Selection
 In Proceedings 17th International Conf. on Machine Learning
, 2000
"... Subproblem generation, solution, and recombination is a standard approach to combinatorial optimization problems. In many settings identifying suitable subproblems is itself a significant component of the technique. Such subproblems are often identified using a heuristic rule. Here we show how ..."
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Cited by 4 (0 self)
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Subproblem generation, solution, and recombination is a standard approach to combinatorial optimization problems. In many settings identifying suitable subproblems is itself a significant component of the technique. Such subproblems are often identified using a heuristic rule. Here we show
Results 1  10
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