Use of machine learning: computational methods, quality rating, data pre-processing

R. Muhamedyev.  Machine learning methods: An overview // Computer Modeling and New Technologies. Scientific and research journal. – Riga, 2015. — 19(6). — P. 14-29.
Abstract
This review covers the vast field of machine learning (ML), and relates to weak artificial intelligence. It includes the 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 bad value elimination methods, normalization. It addresses issues of ML systems application in big data management and the approaches of their solution by methods of computation paralleling and roughening of gradient descent.
Key words: machine learning,  Big Data, regression, map reduce, artificial neural networks, k-NN, SVN, LDAC, DLDA, accuracy, precision, recall, T1 Score.
[http://geoml.info/?p=336]
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