Kernel Association for Classification and Prediction: A Survey

Author

Motai, Yuichi

Author_Institution

Dept. of Electr. & Comput. Eng., Virginia Commonwealth Univ., Richmond, VA, USA

Volume

26

Issue

2

fYear

2015

fDate

Feb. 2015

Firstpage

208

Lastpage

223

Abstract

Kernel association (KA) in statistical pattern recognition used for classification and prediction have recently emerged in a machine learning and signal processing context. This survey outlines the latest trends and innovations of a kernel framework for big data analysis. KA topics include offline learning, distributed database, online learning, and its prediction. The structural presentation and the comprehensive list of references are geared to provide a useful overview of this evolving field for both specialists and relevant scholars.

Keywords

Big Data; data analysis; learning (artificial intelligence); pattern classification; principal component analysis; signal processing; support vector machines; Big Data analysis; KA; PCA; SVM; kernel association; machine learning; pattern classification; pattern prediction; principal component analysis; signal processing; support vector machine; Accuracy; Artificial neural networks; Feature extraction; Kernel; Optimization; Principal component analysis; Support vector machines; Kernel methods; Mercer kernels; neural network (NN); principal component analysis (PCA); support vector machine (SVM); support vector machine (SVM).;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2014.2333664

Filename

6851930