Algorithms for clustering data
Algorithms for clustering data
A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fundamentals of speech recognition
Fundamentals of speech recognition
Speech recognition by machines and humans
Speech Communication
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Automatic Capacity Tuning of Very Large VC-Dimension Classifiers
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Support vector machines for speech recognition
Support vector machines for speech recognition
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Telephone speech recognition using neural networks and hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 01
On the use of support vector machines for phonetic classification
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
Binary tree of SVM: a new fast multiclass training and classification algorithm
IEEE Transactions on Neural Networks
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In this paper we introduce and investigate the performance of a simple framework for multiclass problems of support vector machine (SVM), we present a new architecture named EBTSVM (Efficient Binary Tree Multiclass SVM), in order to achieve high classification efficiency for multiclass problems. The proposed paradigm builds a binary tree for multiclass SVM by genetic algorithms with the aim of obtaining optimal partitions for the optimal tree. Our approach is more accurate in the construction of the tree. Further, in the test phase EBTSVM, due to its Log complexity, it is much faster than other methods in problems that have big class number. In the context of phonetic classification by EBTSVM machine, a recognition rate of 57.54%, on the 20 vowels of TIMIT corpus was achieved. These results are comparable with the state of the arts, in particular the results obtained by SVM with one-versus-one strategy. In addition, training time and number of support vectors, which determine the duration of the tests, are also reduced compared to other methods. However, these results are unacceptably large for the speech recognition task. This calls for the development of more efficient multi-class kernel methods in terms of accuracy and sparsity.