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Classification and Regression Forests
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
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Classification and Regression Forests.
Klaschka, Jan ; Kotrč, Emil
Classification forest is a classification model constructed by combinaning several classification trees. A predictor vector is assigned a class by each of the trees, and the overall classification function is given by majority voting. Similarly, a regression forest consists of several regression trees, and the overall regression function is defined as a weighted average of regression functions of individual trees. Brief explanations of some forest construction methods, namely of bagging, boosting, arcing and Random Forests, are given.
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Methods for Multidimensional Event Classification: A Case Study
Bock, R.K. ; Chilingarian, A. ; Gaug, M. ; Hakl, František ; Hengstebeck, T. ; Jiřina, Marcel ; Klaschka, Jan ; Kotrč, Emil ; Savický, Petr ; Towers, S. ; Vaicilius, A. ; Wittek, W.
Fulltext: content.csg -  PDF
Plný tet: v887-03 - PDF
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