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» Dirichlet Process Mixtures of Generalized Linear Models
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180
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BMCBI
2007
194views more  BMCBI 2007»
15 years 3 months ago
Kernel-imbedded Gaussian processes for disease classification using microarray gene expression data
Background: Designing appropriate machine learning methods for identifying genes that have a significant discriminating power for disease outcomes has become more and more importa...
Xin Zhao, Leo Wang-Kit Cheung
133
Voted
NIPS
2003
15 years 5 months ago
ICA-based Clustering of Genes from Microarray Expression Data
We propose an unsupervised methodology using independent component analysis (ICA) to cluster genes from DNA microarray data. Based on an ICA mixture model of genomic expression pa...
Su-In Lee, Serafim Batzoglou
132
Voted
FSTTCS
2006
Springer
15 years 7 months ago
On Decidability of LTL Model Checking for Process Rewrite Systems
We establish a decidability boundary of the model checking problem for infinite-state systems defined by Process Rewrite Systems (PRS) or weakly extended Process Rewrite Systems (w...
Laura Bozzelli, Mojmír Kretínsk&yacu...
171
Voted
NIPS
2003
15 years 5 months ago
Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data
In this paper we introduce a new underlying probabilistic model for principal component analysis (PCA). Our formulation interprets PCA as a particular Gaussian process prior on a ...
Neil D. Lawrence
131
Voted
ICA
2004
Springer
15 years 9 months ago
Postnonlinear Overcomplete Blind Source Separation Using Sparse Sources
Abstract. We present an approach for blindly decomposing an observed random vector x into f(As) where f is a diagonal function i.e. f = f1 × . . . × fm with one-dimensional funct...
Fabian J. Theis, Shun-ichi Amari