National Repository of Grey Literature 24 records found  beginprevious21 - 24  jump to record: Search took 0.02 seconds. 
Meta-learning
Hovorka, Martin ; Hrabec, Jakub (referee) ; Honzík, Petr (advisor)
Goal of this work is to make acquaintance and study meta-learningu methods, program algorithm and compare with other machine learning methods.
Power Increasing of Serial Combustion Engines
Novák, Pavel ; Svída, David (referee) ; Dundálek, Radim (advisor)
In this bachelor’s thesis is presented suvey of technical resolution for power increasing of serial combustion engines. The work is oriented especially on technical resolution for spark ignition engine and diesel engine. First two chapters says obout way of combustion process and obout creation the fuel mixture of spark ignition engine and diesel engine. In other chapters is described change length of suck pipeline, mechanical and turbo boosting engine and variable valve timing which is depended on the engine load. At the close there is short muse obout future in tecnical development.
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.
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.

National Repository of Grey Literature : 24 records found   beginprevious21 - 24  jump to record:
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