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» Approximate algorithms for neural-Bayesian approaches
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ICANN
2003
Springer
15 years 3 months ago
Expectation-MiniMax Approach to Clustering Analysis
Abstract. This paper proposes a general approach named ExpectationMiniMax (EMM) for clustering analysis without knowing the cluster number. It describes the contrast function of Ex...
Yiu-ming Cheung
NIPS
2007
14 years 11 months ago
A Constraint Generation Approach to Learning Stable Linear Dynamical Systems
Stability is a desirable characteristic for linear dynamical systems, but it is often ignored by algorithms that learn these systems from data. We propose a novel method for learn...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
ICMLA
2007
14 years 11 months ago
Control of a re-entrant line manufacturing model with a reinforcement learning approach
This paper presents the application of a reinforcement learning (RL) approach for the near-optimal control of a re-entrant line manufacturing (RLM) model. The RL approach utilizes...
José A. Ramírez-Hernández, Em...
ISAAC
2005
Springer
127views Algorithms» more  ISAAC 2005»
15 years 3 months ago
Decision Making Based on Approximate and Smoothed Pareto Curves
Abstract. We consider bicriteria optimization problems and investigate the relationship between two standard approaches to solving them: (i) computing the Pareto curve and (ii) the...
Heiner Ackermann, Alantha Newman, Heiko Rögli...
ICCV
2005
IEEE
15 years 3 months ago
A Unifying Approach to Hard and Probabilistic Clustering
We derive the clustering problem from first principles showing that the goal of achieving a probabilistic, or ”hard”, multi class clustering result is equivalent to the algeb...
Ron Zass, Amnon Shashua