|
Recently in the area of machine learning the concept of combining classifiers is proposed as a new direction for the improvement of the performance of individual classifiers. These classifiers could be based on a variety of classification methodologies, and could achieve different rate of correctly classified individuals. The goal of classification result integration algorithms is to generate more certain, precise and accurate system results. Dietterich (2001) provides an accessible and informal reasoning, from statistical, computational and representational viewpoints, of why ensembles can improve results. ==Methods== Numerous methods have been suggested for the creation of ensemble of classifiers. * Using different subset of training data with a single learning method * Using different training parameters with a single training method (e.g. using different initial weights for each neural network in an ensemble) * Using different learning methods. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Ensembles of classifiers」の詳細全文を読む スポンサード リンク
|