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» Approximate algorithms for neural-Bayesian approaches
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ICANN
2003
Springer
15 years 9 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
15 years 5 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
15 years 5 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 10 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 10 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