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Klasifikátor založený na inverzních hodnotách indexů
Jiřina, Marcel ; Jiřina jr., M.
A new method for the classification of data into classes is presented. The method is based on the sum of reciprocals of neighbors' indexes. We show that neighbors' indexes are in close relation to the polynomial transform of the neighbors' distances. The sum of the reciprocals of indexes for all neighbors forms truncated harmonic series due to a finite number of its elements. For the neighbors of one class there is a sum of the selected elements of this truncated series. It is proved that the ratio of these sums gives just the probability that the point to be classified -- the query point -- is of that class. The classification ability is demonstrated on real-life data from the Machine Learning Repository and the results are compared with published results obtained through other methods.
Fulltext: content.csg -  PDF
Plný tet: v1034-08 - PDF
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Klasifikace textových dokumentů
Humpolíček, Jiří
In this report, we propose four feature selection algorithms based on the Best Individual Feature method and one based on the sequential method. After that the best method is selected for following classifier methods comparison. In this step we compare classification performance and computation expense of two classifiers based on Naive Bayes and third classifier is SVM. Classification performance is tested on the Reuters data set and Newsgroup data set. Finally we shows results on the multi-labelled subset of the Reuters data set.
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Noisy-or classifier
Vomlel, Jiří
We discuss application of the noisy-or model to classification with large number of attributes. An example of such a task is categorization of text documents, where attributes are single words from the documents.
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