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An Entropy-based Learning Algorithm of Bayesian Conditional Trees
TL;DR: In this paper, a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition is presented, which directs the user to group digits into several classes consisting of digits that are hard to distinguish and then construct an optimal conditional tree representation for each class of digits instead of for each single digit.
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Abstract: This article offers a modification of Chow and Liu's learning algorithm in the context of handwritten digit recognition. The modified algorithm directs the user to group digits into several classes consisting of digits that are hard to distinguish and then constructing an optimal conditional tree representation for each class of digits instead of for each single digit as done by Chow and Liu (1968). Advantages and extensions of the new method are discussed. Related works of Wong and Wang (1977) and Wong and Poon (1989) which offer a different entropy-based learning algorithm are shown to rest on inappropriate assumptions.
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References
•Book
Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference
Judea Pearl
- 01 Jan 1988
TL;DR: Probabilistic Reasoning in Intelligent Systems as mentioned in this paper is a complete and accessible account of the theoretical foundations and computational methods that underlie plausible reasoning under uncertainty, and provides a coherent explication of probability as a language for reasoning with partial belief.
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Local computations with probabilities on graphical structures and their application to expert systems
TL;DR: The invention comprises pigments of the general Formula I in which each X is hydrogen, chlorine or bromine, A is (a) an aliphatic hydrocarbon radical, (b) the phenylene group or (c) the naphthylene group and R is identical with the 1: 2-phthaloylcarbazole-aminocarbonyl radical shown in the formula.
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Approximating discrete probability distributions with dependence trees
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Probabilistic reasoning in intelligent systems: Networks of plausible inference
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Fusion, propagation, and structuring in belief networks
TL;DR: It is shown that if the network is singly connected (e.g. tree-structured), then probabilities can be updated by local propagation in an isomorphic network of parallel and autonomous processors and that the impact of new information can be imparted to all propositions in time proportional to the longest path in the network.
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