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DAGSTUHL
2009
15 years 7 months ago
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Abstract. In this paper we elaborate on the challenges of learning manifolds that have many relevant clusters, and where the clusters can have widely varying statistics. We call su...
Erzsébet Merényi, Kadim Tasdemir, Li...
BMCBI
2007
215views more  BMCBI 2007»
15 years 6 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
JMLR
2002
111views more  JMLR 2002»
15 years 6 months ago
The Learning-Curve Sampling Method Applied to Model-Based Clustering
We examine the learning-curve sampling method, an approach for applying machinelearning algorithms to large data sets. The approach is based on the observation that the computatio...
Christopher Meek, Bo Thiesson, David Heckerman
ICGI
2010
Springer
15 years 5 months ago
Learning PDFA with Asynchronous Transitions
In this paper we extend the PAC learning algorithm due to Clark and Thollard for learning distributions generated by PDFA to automata whose transitions may take varying time length...
Borja Balle, Jorge Castro, Ricard Gavaldà
ECCV
2002
Springer
16 years 8 months ago
Learning Shape from Defocus
We present a novel method for inferring three-dimensional shape from a collection of defocused images. It is based on the observation that defocused images are the null-space of ce...
Paolo Favaro, Stefano Soatto