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ACL
1998
15 years 7 months ago
Maximum Entropy Model Learning of the Translation Rules
This paper proposes a learning method of translation rules from parallel corpora. This method applies the maximum entropy principle to a probabilistic model of translation rules. ...
Kengo Sato, Masakazu Nakanishi
UAI
2003
15 years 7 months ago
On Local Optima in Learning Bayesian Networks
This paper proposes and evaluates the k-greedy equivalence search algorithm (KES) for learning Bayesian networks (BNs) from complete data. The main characteristic of KES is that i...
Jens D. Nielsen, Tomás Kocka, José M...
NIPS
1996
15 years 7 months ago
Radial Basis Function Networks and Complexity Regularization in Function Learning
In this paper we apply the method of complexity regularization to derive estimation bounds for nonlinear function estimation using a single hidden layer radial basis function netwo...
Adam Krzyzak, Tamás Linder
NIPS
1992
15 years 7 months ago
A Note on Learning Vector Quantization
Vector Quantization is useful for data compression. Competitive Learning which minimizes reconstruction error is an appropriate algorithm for vector quantization of unlabelled dat...
Virginia R. de Sa, Dana H. Ballard
JACIII
2006
97views more  JACIII 2006»
15 years 5 months ago
Opposition-Based Reinforcement Learning
In this paper a method for image segmentation using an opposition-based reinforcement learning scheme is introduced. We use this agent-based approach to optimally find the appropri...
Hamid R. Tizhoosh