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13 years 11 months ago
A Probabilistic Learning Approach to Whole-Genome Operon Prediction
We present a computational approach to predicting operons in the genomes of prokaryotic organisms. Our approach uses machine learning methods to induce predictive models for this ...
Mark Craven, David Page, Jude W. Shavlik, Joseph B...
ICML
2010
IEEE
13 years 11 months ago
Budgeted Nonparametric Learning from Data Streams
We consider the problem of extracting informative exemplars from a data stream. Examples of this problem include exemplarbased clustering and nonparametric inference such as Gauss...
Ryan Gomes, Andreas Krause
ESANN
2008
13 years 11 months ago
Safe exploration for reinforcement learning
In this paper we define and address the problem of safe exploration in the context of reinforcement learning. Our notion of safety is concerned with states or transitions that can ...
Alexander Hans, Daniel Schneegaß, Anton Maxi...
EMO
2009
Springer
159views Optimization» more  EMO 2009»
14 years 4 months ago
Recombination for Learning Strategy Parameters in the MO-CMA-ES
The multi-objective covariance matrix adaptation evolution strategy (MO-CMA-ES) is a variable-metric algorithm for real-valued vector optimization. It maintains a parent population...
Thomas Voß, Nikolaus Hansen, Christian Igel
ICPR
2008
IEEE
14 years 4 months ago
Learning combined similarity measures from user data for image retrieval
Image retrieval has become an interesting and active field due to the increasing necessity of searching and browsing very large image repositories. Images are represented using s...
Miguel Arevalillo-Herráez, Francesc J. Ferr...