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Application of finite mixtures to text document classification
Novovičová, Jana ; Malík, Antonín
Finite mixture modelling of class-conditional distributions is a standard method in a statistical pattern recognition. We proposed to use the mixture of multinomial distributions as a model for class-conditional distribution for text document classification task. The vector document representations using a bag-of-words or a unigram approach are employed. Experimental comparison of the proposed model and the standard models was performed using Reuters-21578 database.
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On the way to learning deterministic objects
Bůcha, Jindřich
The paper deals with learning knowledge about objects, i.e. entities of the real environment. This is an important topic, often neglected by machine learning. The whole experimental approach is implemented via the integration of several areas of AI, namely machine learning, knowledge base management, reasoning, especially Prolog-like and analogical. More specifically, the approach is based on further generalization of already learned (generalized) rules, and on analogy.
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Two applications of Bayesian networks
Vomlel, Jiří
We present two recent applications of Bayesian networks: adaptive testing and troubleshooting man-made devices. We review briefly the underlying theory and provide a general framework for building strategies using Bayesian network models. We discuss applications of the framework to adaptive testing and troubleshooting. The paper is based on our experience with two projects.
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