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CORR
2010
Springer
167views Education» more  CORR 2010»
14 years 10 months ago
Network Flow Algorithms for Structured Sparsity
We consider a class of learning problems that involve a structured sparsityinducing norm defined as the sum of -norms over groups of variables. Whereas a lot of effort has been pu...
Julien Mairal, Rodolphe Jenatton, Guillaume Obozin...
JMLR
2006
136views more  JMLR 2006»
14 years 9 months ago
Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost base...
Tonatiuh Peña Centeno, Neil D. Lawrence
TJS
2008
95views more  TJS 2008»
14 years 9 months ago
Combating I-O bottleneck using prefetching: model, algorithms, and ramifications
Multiple memory models have been proposed to capture the effects of memory hierarchy culminating in the I-O model of Aggarwal and Vitter [?]. More than a decade of architectural a...
Akshat Verma, Sandeep Sen
TON
2008
125views more  TON 2008»
14 years 9 months ago
Two techniques for fast computation of constrained shortest paths
Abstract-- Computing constrained shortest paths is fundamental to some important network functions such as QoS routing, which is to find the cheapest path that satisfies certain co...
Shigang Chen, Meongchul Song, Sartaj Sahni
IJAR
2010
113views more  IJAR 2010»
14 years 8 months ago
A geometric view on learning Bayesian network structures
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, c...
Milan Studený, Jirí Vomlel, Raymond ...