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AAAI
2008
15 years 8 months ago
Bayes-Relational Learning of Opponent Models from Incomplete Information in No-Limit Poker
We propose an opponent modeling approach for no-limit Texas hold-em poker that starts from a (learned) prior, i.e., general expectations about opponent behavior and learns a relat...
Marc J. V. Ponsen, Jan Ramon, Tom Croonenborghs, K...
147
Voted
TREC
2008
15 years 7 months ago
Incorporating Non-Relevance Information in the Estimation of Query Models
: We describe the participation of the University of Amsterdam's ILPS group in the relevance feedback track at TREC 2008. We introduce a new model which incorporates informati...
Edgar Meij, Wouter Weerkamp, Jiyin He, Maarten de ...
NIPS
2004
15 years 7 months ago
Spike-timing Dependent Plasticity and Mutual Information Maximization for a Spiking Neuron Model
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a s...
Taro Toyoizumi, Jean-Pascal Pfister, Kazuyuki Aiha...
ISICT
2003
15 years 7 months ago
Supporting complexity through (informational and physical) collaboration and modeling
Abstract: A vision of tools to support software development organizations in the process of development, maintenance and evolution of systems is presented. The envisioned tools see...
Daniel C. M. May, Bent Bruun Kristensen, Palle Now...
ICML
2000
IEEE
16 years 6 months ago
Maximum Entropy Markov Models for Information Extraction and Segmentation
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...