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» Algorithm Selection using Reinforcement Learning
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133
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COR
2007
106views more  COR 2007»
15 years 1 months ago
On a stochastic sequencing and scheduling problem
We present a framework for solving multistage pure 0–1 programs for a widely used sequencing and scheduling problem with uncertainty in the objective function coefficients, the...
Antonio Alonso-Ayuso, Laureano F. Escudero, M. Ter...
131
Voted
ICML
1999
IEEE
16 years 3 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
136
Voted
IJCNN
2008
IEEE
15 years 8 months ago
Adaptive curiosity for emotions detection in speech
— Exploratory activities seem to be crucial for our cognitive development. According to psychologists, exploration is an intrinsically rewarding behaviour. The developmental robo...
Alexis Bondu, Vincent Lemaire
151
Voted
BMCBI
2008
219views more  BMCBI 2008»
15 years 2 months ago
Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles
Background: Pancreatic cancer is the fourth leading cause of cancer death in the United States. Consequently, identification of clinically relevant biomarkers for the early detect...
Guangtao Ge, G. William Wong
99
Voted
SETN
2004
Springer
15 years 7 months ago
Incremental Mixture Learning for Clustering Discrete Data
Abstract. This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the...
Konstantinos Blekas, Aristidis Likas