Making large-scale support vector machine learning practical
Advances in kernel methods
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
A class-dependent weighted dissimilarity measure for nearest neighbor classification problems
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
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The graphical representation or graphical analysis for multi-dimensional data in multivariate analysis is a very useful method. But it rarely is used to the pattern recognition field. The paper we use the stat plot to represent one observation or sample with multi variances and extract the new graphical features of star plot: sub-area features and sub-barycentre features. The new features are used for the K nearest neighbor classifier (KNN) with leave one out cross validation. Experiments with several standard benchmark data sets show the effectiveness of the new graphical features.