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MCS
2000
Springer
14 years 1 months ago
Ensemble Methods in Machine Learning
Ensemble methods are learning algorithms that construct a set of classi ers and then classify new data points by taking a (weighted) vote of their predictions. The original ensembl...
Thomas G. Dietterich
UAI
2001
13 years 11 months ago
Markov Chain Monte Carlo using Tree-Based Priors on Model Structure
We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure pri...
Nicos Angelopoulos, James Cussens
BMVC
2000
13 years 11 months ago
Parallel Chains, Delayed Rejection and Reversible Jump MCMC for Object Recognition
We tackle the problem of object recognition using a Bayesian approach. A marked point process [1] is used as a prior model for the (unknown number of) objects. A sample is generat...
M. Harkness, P. Green
FLAIRS
1998
13 years 11 months ago
Decision Making in Qualitative Influence Diagrams
The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex...
Silja Renooij, Linda C. van der Gaag
WWW
2001
ACM
14 years 10 months ago
Finding authorities and hubs from link structures on the World Wide Web
Recently, there have been a number of algorithms proposed for analyzing hypertext link structure so as to determine the best "authorities" for a given topic or query. Wh...
Allan Borodin, Gareth O. Roberts, Jeffrey S. Rosen...