Title :
Kernel Association for Classification and Prediction: A Survey
Author_Institution :
Dept. of Electr. & Comput. Eng., Virginia Commonwealth Univ., Richmond, VA, USA
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).;
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
DOI :
10.1109/TNNLS.2014.2333664
