Toy problem

In scientific disciplines, a toy problem is a problem that is not of immediate scientific interest, yet is used as an expository device to illustrate… (More)
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Papers overview

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2017
2017
We give here a comparison of the expected outcome theory, the expected utility theory, and the Bayesian decision theory, by way… (More)
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2010
2010
In Green's function diakoptics, wave-field interactions between disjoint domains in space are described in terms of interacting… (More)
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Highly Cited
2009
Highly Cited
2009
In the past few years, video annotation has benefited a lot from the progress of machine learning techniques. Recently, graph… (More)
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Highly Cited
2009
Highly Cited
2009
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. Our approach is… (More)
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2006
2006
You get to toss coins, each of which is either of type H, having probabilities favouring heads {(1−f), f} or of type T, with… (More)
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Highly Cited
2005
Highly Cited
2005
In the last decades, a large family of algorithms - supervised or unsupervised; stemming from statistic or geometry theory - have… (More)
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Highly Cited
2001
Highly Cited
2001
This report presents a SVM like learning system to handle multi-label problems. Such problems arise naturally in bio-informatics… (More)
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Highly Cited
1998
Highly Cited
1998
Latent variable models represent the probability density of data in a space of several dimensions in terms of a smaller number of… (More)
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1997
1997
  • Marco Budinich
  • 1997
combinatorial optimization is an active field of research in Neural Networks. Since the first attempts to solve the travelling… (More)
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Highly Cited
1990
Highly Cited
1990
Conventional blind search techniques generally assume that the goal nodes for a given problem are distributed randomly along the… (More)
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