Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
A geometric framework for machine learning
A geometric framework for machine learning
The nature of statistical learning theory
The nature of statistical learning theory
Combining classifiers in text categorization
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Prior knowledge in support vector kernels
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
A patent search and classification system
Proceedings of the fourth ACM conference on Digital libraries
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
A Theory of Learning and Generalization: With Applications to Neural Networks and Control Systems
Numerical Methods, Software and Analysis
Numerical Methods, Software and Analysis
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
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We present a new method for constructing nonlinear classifiers. Given any two distinct sets of points in Rn the new method can construct, using gauge group techniques, a closed form expression of a surface, Φ(x) = 0, which separates the two sets. We also show that any two distinct sets of points in Rn can be separated by a polynomial surface and present an algorithm for constructing such polynomial surfaces. Finally we present two numerical examples to illustrate the new method.