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132
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ICGI
1998
Springer
15 years 8 months ago
Learning Stochastic Finite Automata from Experts
We present in this paper a new learning problem called learning distributions from experts. In the case we study the experts are stochastic deterministic finite automata (sdfa). W...
Colin de la Higuera
140
Voted
ICRA
2007
IEEE
189views Robotics» more  ICRA 2007»
15 years 10 months ago
Context Estimation and Learning Control through Latent Variable Extraction: From discrete to continuous contexts
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
Georgios Petkos, Sethu Vijayakumar
115
Voted
ICML
2010
IEEE
15 years 1 months ago
Approximate Predictive Representations of Partially Observable Systems
We provide a novel view of learning an approximate model of a partially observable environment from data and present a simple implemenf the idea. The learned model abstracts away ...
Monica Dinculescu, Doina Precup
126
Voted
UAI
2008
15 years 5 months ago
Learning Hidden Markov Models for Regression using Path Aggregation
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for re...
Keith Noto, Mark Craven
136
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ISMB
2004
15 years 5 months ago
Predicting gene regulation by sigma factors in Bacillus subtilis from genome-wide data
Motivation: Sigma factors regulate the expression of genes in Bacillus subtilis at the transcriptional level. First we assess the ability of currently available gene regulatory ne...
Michiel J. L. de Hoon, Yuko Makita, Seiya Imoto, K...