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ISNN
2011
Springer
12 years 10 months ago
Orthogonal Feature Learning for Time Series Clustering
This paper presents a new method that uses orthogonalized features for time series clustering and classification. To cluster or classify time series data, either original data or...
Xiaozhe Wang, Leo Lopes
ICML
2005
IEEE
14 years 8 months ago
Active learning for sampling in time-series experiments with application to gene expression analysis
Many time-series experiments seek to estimate some signal as a continuous function of time. In this paper, we address the sampling problem for such experiments: determining which ...
Rohit Singh, Nathan Palmer, David K. Gifford, Bonn...
FLAIRS
1998
13 years 9 months ago
Learning to Race: Experiments with a Simulated Race Car
Our focus is on designing adaptable agents for highly dynamic environments. Wehave implementeda reinforcement learning architecture as the reactive componentof a twolayer control ...
Larry D. Pyeatt, Adele E. Howe
WWW
2005
ACM
14 years 8 months ago
Learning domain ontologies for Web service descriptions: an experiment in bioinformatics
The reasoning tasks that can be performed with semantic web service descriptions depend on the quality of the domain ontologies used to create these descriptions. However, buildin...
Marta Sabou, Chris Wroe, Carole A. Goble, Gilad Mi...
CIG
2006
IEEE
14 years 1 months ago
Monte-Carlo Go Reinforcement Learning Experiments
Abstract— This paper describes experiments using reinforcement learning techniques to compute pattern urgencies used during simulations performed in a Monte-Carlo Go architecture...
Bruno Bouzy, Guillaume Chaslot