Title :
Adaptive training methods for optimal margin classification
Author :
Lehtokangas, Mikko
Author_Institution :
Signal Process. Lab., Tampere Univ. of Technol., Finland
Abstract :
The concept of optimal hyperplane has been recently investigated in the context of statistical learning theory. The important property of an optimal hyperplane is that it provides maximum margins to each class to be separated. Obviously, such a decision boundary is expected to yield good generalization. In neural network learning techniques, the majority of them do not make use of the optimal hyperplane concept. As a result, in many cases extensive tuning is required to reach good generalization. In this study we consider adaptive training schemes for optimal margin classification with neural networks. We describe some novel schemes and compare them with the conventional schemes. Simple experiments are presented to demonstrate the performance of each scheme
Keywords :
adaptive systems; generalisation (artificial intelligence); learning (artificial intelligence); neural nets; optimisation; pattern classification; adaptive learning; decision boundary; generalization; neural network; optimal hyperplane; optimisation; statistical learning; Adaptive signal processing; Laboratories; Machine learning; Multi-layer neural network; Multilayer perceptrons; Neural networks; Radial basis function networks; Statistical learning; Support vector machine classification; Support vector machines;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.831171