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» On learning algorithm selection for classification
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121
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ICML
1999
IEEE
16 years 1 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
109
Voted
COLT
2007
Springer
15 years 7 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron
114
Voted
GECCO
2010
Springer
153views Optimization» more  GECCO 2010»
15 years 4 months ago
Multi-task evolutionary shaping without pre-specified representations
Shaping functions can be used in multi-task reinforcement learning (RL) to incorporate knowledge from previously experienced tasks to speed up learning on a new task. So far, rese...
Matthijs Snel, Shimon Whiteson
100
Voted
CEEMAS
2005
Springer
15 years 6 months ago
Case-Based Student Modeling in Multi-agent Learning Environment
Abstract. The student modeling (SM) is a core component in the development of Intelligent Learning Environments (ILEs). In this paper we describe how a Multi-agent Intelligent Lear...
Carolina González, Juan C. Burguillo-Rial, ...
123
Voted
IJSI
2008
156views more  IJSI 2008»
15 years 1 months ago
Co-Training by Committee: A Generalized Framework for Semi-Supervised Learning with Committees
Many data mining applications have a large amount of data but labeling data is often difficult, expensive, or time consuming, as it requires human experts for annotation. Semi-supe...
Mohamed Farouk Abdel Hady, Friedhelm Schwenker