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Highly Cited

2009

Highly Cited

2009

A machine learning approach to learning to rank trains a model to optimize a target evaluation measure with repect to training… Expand

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Highly Cited

2009

Highly Cited

2009

In many image and video collections, we have access only to partially labeled data. For example, personal photo collections often… Expand

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Highly Cited

2006

Highly Cited

2006

The optimal detection of signals requires detailed knowledge of the noise statistics. In many applications, the assumption of… Expand

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Highly Cited

2004

Highly Cited

2004

In this article, we study Pareto local optimum sets for the biobjective Traveling Salesman Problem applying straightforward… Expand

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Highly Cited

2002

Highly Cited

2002

We consider the problem of designing a virtual topology to minimize electronic routing, that is, grooming traffic, in wavelength… Expand

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Highly Cited

2001

Highly Cited

2001

We describe new algorithms of the locally optimal block preconditioned conjugate gradient (LOBPCG) method for symmetric… Expand

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Highly Cited

2001

Highly Cited

2001

This paper analyzes the performance implications of interorganizational relationships in the development of technological… Expand

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Highly Cited

1999

Highly Cited

1999

Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and… Expand

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Highly Cited

1997

Highly Cited

1997

A problem in the transmission of analog information across a noisy discrete channel is the choice of channel code rate that… Expand

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Highly Cited

1985

Highly Cited

1985

We investigate the complexity of finding locally optimal solutions to NP-hard combinatorial optimization problems. Local… Expand

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