Regularization theory and neural networks architectures
Neural Computation
Machine Learning
Data preparation for data mining
Data preparation for data mining
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Computers and Industrial Engineering
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This paper presents some empirical results showing that simple attribute scaling in the data preprocessing stage can improve the performance of linear binary classifiers In particular, a class specific scaling method that utilises information about the class distribution of the training sample can significantly improve classification accuracy This form of scaling can boost the performance of a simple centroid classifier to similar levels of accuracy as the more complex, and computationally expensive, support vector machine and regression classifiers Further, when SVMs are used, scaled data produces better results, for smaller amounts of training data, and with smaller regularisation constant values, than unscaled data.