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Logic learning machine : ウィキペディア英語版 | Logic learning machine
Logic Learning Machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, developed by Marco Muselli, from the Italian National Research Council. Logic Learning Machine is implemented in the Rulex suite. LLM has been employed in different fields, including orthopaedic patient classification, DNA microarray analysis and Clinical Decision Support System. == History ==
The Switching Neural Network approach was developed in the 1990s to overcome the drawbacks of the most commonly used machine learning methods. In particular, black box methods, such as multilayer perceptron and support vector machine, had good accuracy but could not provide deep insight into the studied phenomenon. On the other hand, decision trees were able to describe the phenomenon but often lacked accuracy. Switching Neural Networks made use of Boolean algebra to build sets of intelligible rules able to obtain very good performance. In 2014, an efficient version of Switching Neural Network was developed and implemented in the Rulex suite with the name Logic Learning Machine.〔(【引用サイトリンク】publisher=Italian National Research Council )〕 Also a LLM version devoted to regression problems was developed.
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「Logic learning machine」の詳細全文を読む
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