Proceedings ArticleDOI
Binary Classification by SVM based neural Trees and Nonlinear SVMs
M.A. Kumar,M. Gopal +1 more
- pp 383-387
Reads0
Chats0
TLDR
It is observed that nonlinear SVMs are more effective, though at higher computational cost, than SVM based neural trees, which will provide important guidelines in data mining applications on real world datasets.Abstract:
When performing classification of large set of samples, neural trees (NTs) are preferably used. To circumvent the problem of poor generalization of neural trees, hybrid neural trees have been proposed. Recently hybrid SVM based neural tree has been shown to be an effective binary classifier. In this paper, we examine the performance of SVM based neural trees relative to the nonlinear SVMs. We observe that nonlinear SVMs are more effective, though at higher computational cost. Our conclusions will provide important guidelines in data mining applications on real world datasetsread more
References
More filters
Journal ArticleDOI
A Tutorial on Support Vector Machines for Pattern Recognition
TL;DR: There are several arguments which support the observed high accuracy of SVMs, which are reviewed and numerous examples and proofs of most of the key theorems are given.
Journal ArticleDOI
A comparison of methods for multiclass support vector machines
Hsu Chih-Wei,Chih-Jen Lin +1 more
TL;DR: Decomposition implementations for two "all-together" multiclass SVM methods are given and it is shown that for large problems methods by considering all data at once in general need fewer support vectors.
Journal ArticleDOI
Robust linear programming discrimination of two linearly inseparable sets
TL;DR: A single linear programming formulation is proposed which generates a plane that of minimizes an average sum of misclassified points belonging to two disjoint points sets in n-dimensional real space, without the imposition of extraneous normalization constraints that inevitably fail to handle certain cases.
Journal ArticleDOI
Feedforward Neural Network Construction Using Cross Validation
TL;DR: An algorithm that constructs feedforward neural networks with a single hidden layer for pattern classification that is effective in obtaining networks with predictive accuracy rates that are better than those obtained by state-of-the-art decision tree methods.