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» Bayesian multiscale analysis for time series data
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NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 6 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani
BIOINFORMATICS
2005
151views more  BIOINFORMATICS 2005»
13 years 7 months ago
Differential and trajectory methods for time course gene expression data
Motivation: The issue of high dimensionality in microarray data has been, and remains, a hot topic in statistical and computational analysis. Efficient gene filtering and differen...
Yulan Liang, Bamidele Tayo, Xueya Cai, Arpad Kelem...
PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
13 years 6 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas
BMCBI
2008
154views more  BMCBI 2008»
13 years 7 months ago
Bayesian models and meta analysis for multiple tissue gene expression data following corticosteroid administration
Background: This paper addresses key biological problems and statistical issues in the analysis of large gene expression data sets that describe systemic temporal response cascade...
Yulan Liang, Arpad Kelemen
KDD
2002
ACM
171views Data Mining» more  KDD 2002»
14 years 8 months ago
Mining complex models from arbitrarily large databases in constant time
In this paper we propose a scaling-up method that is applicable to essentially any induction algorithm based on discrete search. The result of applying the method to an algorithm ...
Geoff Hulten, Pedro Domingos