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Classification using Hierarchical Naïve Bayes models
 Machine Learning 2006
, 2002
"... Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well performing set of classifiers is the Nave Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an in ..."
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Cited by 14 (1 self)
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Classification problems have a long history in the machine learning literature. One of the simplest, and yet most consistently well performing set of classifiers is the Nave Bayes models. However, an inherent problem with these classifiers is the assumption that all attributes used to describe an instance are conditionally independent given the class of that instance. When this assumption is violated (which is often the case in practice) it can reduce classification accuracy due to "information doublecounting" and interaction omission.
Supervised Naive Bayes Parameters
, 2002
"... this paper we show, how this supervised learning problem can be solved e#ciently. We introduce an alternative parametrization in which the supervised likelihood becomes concave. From this result it follows that there can be at most one maximum, easily found by local optimization methods. We present ..."
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Cited by 4 (2 self)
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this paper we show, how this supervised learning problem can be solved e#ciently. We introduce an alternative parametrization in which the supervised likelihood becomes concave. From this result it follows that there can be at most one maximum, easily found by local optimization methods. We present test results that show this is feasible and highly beneficial
LEARNING BAYESIAN NETWORKS FROM DATA: STRUCTURE OPTIMIZATION AND PARAMETER ESTIMATION
"... of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion ..."
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of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author’s prior written permission. Date:
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"... We consider the problem of learning Bayesian network classifiers that maximize the margin over a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parame ..."
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We consider the problem of learning Bayesian network classifiers that maximize the margin over a set of classification variables. We find that this problem is harder for Bayesian networks than for undirected graphical models like maximum margin Markov networks. The main difficulty is that the parameters in a Bayesian network must satisfy additional normalization constraints that an undirected graphical model need not respect. These additional constraints complicate the optimization task. Nevertheless, we derive an effective training algorithm that solves the maximum margin training problem for a range of Bayesian network topologies, and converges to an approximate solution for arbitrary network topologies. Experimental results show that the method can demonstrate improved generalization performance over Markov networks when the directed graphical structure encodes relevant knowledge. In practice, the training technique allows one to combine prior knowledge expressed as a directed (causal) model with state of the art discriminative learning methods. 1
Probabilistic, InformationTheoretic Models for Etymological Alignment
"... This thesis starts out by reviewing Bayesian reasoning and Bayesian network models. We present results related to discriminative learning of Bayesian network parameters. Along the way, we explicitly identify a number of problems arising in Bayesian model class selection. This leads us to informati ..."
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This thesis starts out by reviewing Bayesian reasoning and Bayesian network models. We present results related to discriminative learning of Bayesian network parameters. Along the way, we explicitly identify a number of problems arising in Bayesian model class selection. This leads us to information theory and, more specifically, the minimum description length (MDL) principle. We look at its theoretic foundations and practical implications. The MDL approach provides elegant solutions for the problem of model class selection and enables us to objectively compare any set of models, regardless of their parametric structure. Finally, we apply these methods to problems arising in computational etymology. We develop model families for the task of soundbysound alignment across kindred languages.