Hyperplane Algorithm – First Step of the Paired Planes Classification Procedure

D. Vance and A.L. Ralescu (USA)


Artificial intelligence, machine learning, supervised learning, pattern recognition, and support vector machines.


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.

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