Machine Learning by Adaptive Local Hyperplane Algorithm - Yang, Tao;Kecman, Vojislav
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Machine learning algorithms play a key role in many fields of science, technology and business. Classification and regression are two fundamental tasks in machine learning. This book is devoted to develop learning algorithms for solving the classification and regression problems. A novel learning algorithm dubbed as adaptive local hyperplane (ALH) is introduced to solve the general classification and regression problems. The ALH algorithm belongs to the nonparametric paradigm and it is an extension of the K-local hyperplane distance nearest neighbor (HKNN) algorithm. The ALH algorithm and its…mehr

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Produktbeschreibung
Machine learning algorithms play a key role in many fields of science, technology and business. Classification and regression are two fundamental tasks in machine learning. This book is devoted to develop learning algorithms for solving the classification and regression problems. A novel learning algorithm dubbed as adaptive local hyperplane (ALH) is introduced to solve the general classification and regression problems. The ALH algorithm belongs to the nonparametric paradigm and it is an extension of the K-local hyperplane distance nearest neighbor (HKNN) algorithm. The ALH algorithm and its extensions have been successfully applied into many real world tasks such as face recognition and DNA microarray analysis and protein sequence function prediction.
Autorenporträt
Dr Tao Yang is a research fellow at the University of Technology Sydney, Australia. He has published 4 journal papers and 7 international conference papers in the area of machine learning (data mining). Vojislav Kecman is with VCU, SoE, CS, Richmond, VA, USA. He authored several books on machine learning (see, www.people.vcu.edu/~vkecman).