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» Learning for control from multiple demonstrations
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FLAIRS
2004
14 years 11 months ago
Developing Task Specific Sensing Strategies Using Reinforcement Learning
Robots that can adapt and perform multiple tasks promise to be a powerful tool with many applications. In order to achieve such robots, control systems have to be constructed that...
Srividhya Rajendran, Manfred Huber
HIS
2004
14 years 11 months ago
Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
: This work presents a new hybrid neuro-fuzzy model for automatic learning of actions taken by agents. The main objective of this new model is to provide an agent with intelligence...
Karla Figueiredo, Marley B. R. Vellasco, Marco Aur...
ICASSP
2010
IEEE
14 years 9 months ago
Multiplicative update rules for nonnegative matrix factorization with co-occurrence constraints
Nonnegative matrix factorization (NMF) is a widely-used tool for obtaining low-rank approximations of nonnegative data such as digital images, audio signals, textual data, financ...
Steven K. Tjoa, K. J. Ray Liu
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RECOMB
2008
Springer
15 years 10 months ago
Spectrum Fusion: Using Multiple Mass Spectra for De Novo Peptide Sequencing
We report on a new algorithm for combining the information from several mass spectra of the same peptide. The algorithm automatically learns peptide fragmentation patterns, so that...
Ritendra Datta, Marshall W. Bern
SMC
2007
IEEE
118views Control Systems» more  SMC 2007»
15 years 3 months ago
One-class learning with multi-objective genetic programming
One-class classification naturally only provides one class of exemplars on which to construct the classification model. In this work, multiobjective genetic programming (GP) all...
Robert Curry, Malcolm I. Heywood