Statistical analysis of big data: An approach based on support vector machines for classification and regression problems

被引：3

作者：

Kadyrova N.O. ^{[1
]}

Pavlova L.V. ^{[1
]}

机构：

[1] Institute of Applied Mathematics and Mechanics, St. Petersburg State Polytechnical University, St. Petersburg

来源：

Biophysics | 2014年 / 59卷 / 3期

关键词：

algorithms based on support vector machines; big data; binary classification; kernel functions; regression; support vector machines;

D O I：

10.1134/S0006350914030105

中图分类号：

学科分类号：

摘要：

A new type of learning algorithms with the supervisor for estimating multidimensional functions is considered. These methods based on Support Vector Machines are widely used due to their ability to deal with high-dimensional and large datasets, and their flexibility in modeling diverse sources of data. Support vector machines and related kernel methods are extremely good at solving prediction problems in computational biology. A background about statistical learning theory and kernel feature spaces is given including practical and algorithmic considerations. © 2014 Pleiades Publishing, Inc.

引用

页码：364 / 373

页数：9

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