Machine learning methods: An overview
Ravil I Muhamedyev
COMPUTER MODELLING & NEW TECHNOLOGIES 2015 19(6) 14-29
Institute of Problems of Information and Control, Ministry of Education and Science of the Republic of Kazakhstan. Pushkina 125, Almaty Kazakhstan
High School of management information systems ISMA, Lomonosov st. 1, Riga, Latvia
This review covers the vast field of machine learning (ML), and relates to weak artificial intelligence. It includes the taxonomy of ML algorithms, setup diagram of machine learning methods, the formal statement of ML and some frequently used algorithms (regressive, artificial neural networks, k-NN, SVN, LDAC, DLDA). It describes classification accuracy indicators, the use of “learning curves” for assessment of ML methods and data pre-processing methods, including methods of abnormal values elimination and normalization. It addresses issues of application of ML systems at the processing of big data and the approaches of their solution by methods of parallel computing, mapreduce and modification of gradient descent.