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622
Learning the Kernel Matrix with SemiDefinite Programming
, 2002
"... Kernelbased learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information ..."
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Cited by 775 (21 self)
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problems in machine learning. In this paper we show how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques. When applied
Method of Approximate Centers for SemiDefinite Programming
, 1996
"... The success of interior point algorithms for largescale linear programming has prompted researchers to extend these algorithms to the semidefinite programming (SDP) case. In this paper, the method of approximate centers of Roos and Vial [13] is extended to SDP. Key words: Semidefinite programmi ..."
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Cited by 3 (3 self)
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The success of interior point algorithms for largescale linear programming has prompted researchers to extend these algorithms to the semidefinite programming (SDP) case. In this paper, the method of approximate centers of Roos and Vial [13] is extended to SDP. Key words: Semidefinite
Using Semidefinite Programming to Enhance
"... Abstract. Supertree methods are used to construct a large tree over a large set of taxa, from a set of small trees over overlapping subsets of the complete taxa set. Since accurate reconstruction methods are currently limited to a maximum of few dozens of taxa, the use of a supertree method in order ..."
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approach that guarantees the properties provided by the current methods and give experimental evidence that it significantly outperforms currently used methods. This method is based on divide and conquer where we use a semidefinite programming approach in the divide step. 1
Using SemiDefinite Programming for Controller . . .
 SIAG/OPT VIEWS AND NEWS, (8):15, FALL
, 1996
"... ..."
SDPARA: SemiDefinite Programming Algorithm PARAllel version
 Parallel Computing
, 2003
"... The SDPA (SemiDefinite Programming Algorithm) is known as efficient computer software based on primaldual interiorpoint method for solving SDPs (Semidefinite Programs). In many applications, however, some SDPs become larger and larger, too large for the SDPA to solve on a single processor. In exec ..."
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Cited by 30 (9 self)
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The SDPA (SemiDefinite Programming Algorithm) is known as efficient computer software based on primaldual interiorpoint method for solving SDPs (Semidefinite Programs). In many applications, however, some SDPs become larger and larger, too large for the SDPA to solve on a single processor
Ensemble Pruning Via Semidefinite Programming
 Journal of Machine Learning Research
, 2006
"... An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead to extra memory usage, computational costs, and occasional decreases in effectiveness. The purpose of ensemble pruning is ..."
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Cited by 42 (2 self)
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. By applying semidefinite programming (SDP) as a solution technique, we are able to get better approximate solutions. Computational experiments show that this SDPbased pruning algorithm outperforms other heuristics in the literature. Its application in a classifiersharing study also demonstrates
Numerical Evaluation of SDPA (SemiDefinite Programming Algorithm).
, 1998
"... . SDPA (SemiDefinite Programming Algorithm) is a C++ implementation of a Mehrotratype primaldual predictorcorrector interiorpoint method for solving the standard form semidefinite program and its dual. We report numerical results of large scale problems to evaluate its performance, and investiga ..."
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Cited by 36 (12 self)
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. SDPA (SemiDefinite Programming Algorithm) is a C++ implementation of a Mehrotratype primaldual predictorcorrector interiorpoint method for solving the standard form semidefinite program and its dual. We report numerical results of large scale problems to evaluate its performance
Using SemiDefinite Programming to Enhance Supertree Resolvability
, 2005
"... Supertree methods are used to construct a large tree over a large set of taxa, from a set of small trees over overlapping subsets of the complete taxa set. Since accurate reconstruction methods are currently limited to a maximum of few dozens of taxa, the use of a supertree method in order to const ..."
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Cited by 4 (1 self)
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that guarantees the properties provided by the current methods and give experimental evidence that it significantly outperforms currently used methods. This method is based on divide and conquer where we use a semidefinite programming approach in the divide step.
Quantum query complexity and semidefinite programming
 In Proceedings of the 18th IEEE Annual Conference on Computational Complexity
, 2003
"... We reformulate quantum query complexity in terms of inequalities and equations for a set of positive semidefinite matrices. Using the new formulation we: 1. show that the workspace of a quantum computer can be limited to at most n + k qubits (where n and k are the number of input and output bits res ..."
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Cited by 30 (1 self)
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be attained by a quantum query algorithm attempts to evaluate f in t queries. 3. use semidefinite programming duality to formulate a dual SDP ˆ P (f,t,ɛ) that is feasible if and only if f can not be evaluated within error ɛ by a tstep quantum query algorithm Using this SDP we derive a general lower bound
Results 1  10
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622