Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Nonlinear Markov networks for continuous variables
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Learning Bayesian Networks
Scalable pseudo-likelihood estimation in hybrid random fields
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Distribution-free learning of Bayesian network structure in continuous domains
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
The Nonparanormal: Semiparametric Estimation of High Dimensional Undirected Graphs
The Journal of Machine Learning Research
Learning multiple tasks with boosted decision trees
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Hybrid random fields are a recently proposed graphical model for pseudo-likelihood estimation in discrete domains. In this paper, we develop a continuous version of the model for nonparametric density estimation. To this aim, Nadaraya-Watson kernel estimators are used to model the local conditional densities within hybrid random fields. First, we introduce a heuristic algorithm for tuning the kernel bandwidhts in the conditional density estimators. Second, we propose a novel method for initializing the structure learning algorithm originally employed for hybrid random fields, which was meant instead for discrete variables. In order to test the accuracy of the proposed technique, we use a number of synthetic pattern classification benchmarks, generated from random distributions featuring nonlinear correlations between the variables. As compared to state-of-the-art nonparametric and semiparametric learning techniques for probabilistic graphical models, kernel-based hybrid random fields regularly outperform each considered alternative in terms of recognition accuracy, while preserving the scalability properties (with respect to the number of variables) that originally motivated their introduction.