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This paper describes a technique for learning both the number of states and the topologyof Hidden Markov Models from examples. The inductionprocess starts with the most specific m...
We describe a framework for inducing probabilistic grammars from corpora of positive samples. First, samples are incorporated by adding ad-hoc rules to a working grammar; subseque...
Many vision tasks can be formulated as partitioning an adjacency graph through optimizing a Bayesian posterior probability p defined on the partition-space. In this paper two appr...