CLASSIFICATION SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA

CLASSIFICATION SUPERVISED LEARNING WITH HIGH-DIMENSIONAL BIOLOGICAL DATA 

Department of Applied Mathematics and Statistics, SUNY at Stony Brook, 

Department of Mathematics and Statistics, California State University, 

There are a number of classification methods for analyzing data, including artificial neural (ANNs see Beale and Jackson, 1990) networks, -nearest-neighbor (fe-NN) methods, decision trees, support vector machines (SVMs), and Fisher s linear discriminant analysis (LDA). Among these methods, a decision tree is a flow-chart-like tree stmcture. An intermediate node denotes a test on a predictive attribute, and a branch represents an outcome of the test. A terminal node denotes class distribution. 

Statistical Bioinformatics A Guide for Life and Biomedical Science Researchers. Edited by Jae K. Lee Copyright 2010 Wiley-Blackwell 

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