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JMLR
2006
169views more  JMLR 2006»
15 years 5 months ago
Bayesian Network Learning with Parameter Constraints
The task of learning models for many real-world problems requires incorporating domain knowledge into learning algorithms, to enable accurate learning from a realistic volume of t...
Radu Stefan Niculescu, Tom M. Mitchell, R. Bharat ...
ICDM
2009
IEEE
152views Data Mining» more  ICDM 2009»
15 years 2 months ago
A Sparsification Approach for Temporal Graphical Model Decomposition
Temporal causal modeling can be used to recover the causal structure among a group of relevant time series variables. Several methods have been developed to explicitly construct te...
Ning Ruan, Ruoming Jin, Victor E. Lee, Kun Huang
GECCO
2004
Springer
132views Optimization» more  GECCO 2004»
15 years 10 months ago
Robust and Efficient Genetic Algorithms with Hierarchical Niching and a Sustainable Evolutionary Computation Model
This paper proposes a new niching method named hierarchical niching, which combines spatial niching in search space and a continuous temporal niching concept. The method is natural...
Jianjun Hu, Erik D. Goodman
UAI
2008
15 years 6 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
ML
2007
ACM
15 years 4 months ago
Feature space perspectives for learning the kernel
In this paper, we continue our study of learning an optimal kernel in a prescribed convex set of kernels, [18]. We present a reformulation of this problem within a feature space e...
Charles A. Micchelli, Massimiliano Pontil