An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to variable and feature selection
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Improved kernel learning using smoothing parameter based linear kernel
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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The objective of supervised learning is to estimate unknowns based on labeled training samples. If the unknown to be estimated is categorical or discrete, the problem is one of classification. Algorithms for supervised learning are useful tools in many areas of agriculture, medicine, and engineering, including prediction of malignant cancer, document analysis, and speech recognition. In general, Support Vector Machine algorithms have been successful in classification problems, but they have high computational complexity. In this paper, we present the Hyperplane Algorithm. It and two other related algorithms form an ensemble classifier for supervised classification. The Hyperplane Algorithm is reminiscent of a support vector machine but is low in computational complexity. It also has several other advantages compared to Support Vector Machines. Results for five real-life datasets results are shown.