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ICML
2005
IEEE
16 years 4 months ago
Learning Gaussian processes from multiple tasks
We consider the problem of multi-task learning, that is, learning multiple related functions. Our approach is based on a hierarchical Bayesian framework, that exploits the equival...
Kai Yu, Volker Tresp, Anton Schwaighofer
GCB
2004
Springer
113views Biometrics» more  GCB 2004»
15 years 9 months ago
Feature Based Representation and Detection of Transcription Factor Binding Sites
: The prediction of transcription factor binding sites is an important problem, since it reveals information about the transcriptional regulation of genes. A commonly used represen...
Rainer Pudimat, Ernst Günter Schukat-Talamazz...
CONTEXT
2001
Springer
15 years 8 months ago
Belief Expansion, Contextual Fit, and the Reliability of Information Sources
We develop a probabilistic criterion for belief expansion that is sensitive to the degree of contextual fit of the new information to our belief set as well as to the reliability...
Luc Bovens, Stephan Hartmann
SDM
2004
SIAM
218views Data Mining» more  SDM 2004»
15 years 5 months ago
Mixture Density Mercer Kernels: A Method to Learn Kernels Directly from Data
This paper presents a method of generating Mercer Kernels from an ensemble of probabilistic mixture models, where each mixture model is generated from a Bayesian mixture density e...
Ashok N. Srivastava
COGSCI
2008
129views more  COGSCI 2008»
15 years 4 months ago
A Rational Analysis of Rule-Based Concept Learning
We propose a new model of human concept learning that provides a rational analysis for learning of feature-based concepts. This model is built upon Bayesian inference for a gramma...
Noah D. Goodman, Joshua B. Tenenbaum, Jacob Feldma...