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- Finnegan Southey, Bret Hoehn, Robert C. Holte
- Machine Learning
- 2005

Uncertainty in poker stems from two key sources, the shuffled deck and an adversary whose strategy is unknown. One approach to playing poker is to find a pessimistic game-theoretic solution (i.e., a Nash equilibrium), but human players have idiosyncratic weaknesses that can be exploited if some model or counter-strategy can be learned by observing their… (More)

- Idanis Diaz, Pierre Boulanger, Russell Greiner, Bret Hoehn, Lindsay Rowe, Albert Murtha
- Conference proceedings : ... Annual International…
- 2013

This paper introduces an automatic brain tumor segmentation method (ABTS) for segmenting multiple components of brain tumor using four magnetic resonance image modalities. ABTS's four stages involve automatic histogram multi-thresholding and morphological operations including geodesic dilation. Our empirical results, on 16 real tumors, show that ABTS works… (More)

An algorithm is presented for fully automated brain tumor segmentation from only two magnetic resonance image modalities. The technique is based on three steps: (1) alternating different levels of automatic histogram-based multi-thresholding step, (2) performing an effective and fully automated procedure for skull-stripping by evolving deformable contours,… (More)

- John Lees-Miller, Fraser Anderson, Bret Hoehn, Russell Greiner
- 2008 Seventh International Conference on Machine…
- 2008

We explore several ways to estimate movie similarity from the free encyclopedia Wikipedia with the goal of improving our predictions for the Netflix Prize. Our system first uses the content and hyperlink structure of Wikipedia articles to identify similarities between movies. We then predict a user's unknown ratings by using these similarities in… (More)

A Bayesian belief network models a joint distribution with an directed acyclic graph representing dependencies among variables and network parameters characterizing conditional distributions. The parameters are viewed as random variables to quantify uncertainty about their values. Belief nets are used to compute responses to queries; i.e., conditional… (More)

A Bayesian network is a model of a distribution, encoded using a network structure S augmented with conditional distribution parameters (CDP) Θ that specify the conditional probability of a variable, given each assignment to its parents. Given a fixed structure S and CDP Θ, we can compute the response to a fixed query Q(Θ) = PS,Θ( C = c |E = e ), which is a… (More)

Opponent modelling is an important issue in games programming today. Programs which do not perform opponent modelling are unlikely to take full advantage of the mistakes made by an opponent. Additionally, programs which do not adapt over time become less of a challenge to players, causing these players to lose interest. While opponent modelling can be a… (More)

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