A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Intelligent Systems
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik-Chervonenkis Dimension
IEEE Transactions on Knowledge and Data Engineering
A time-series-based feature extraction approach for prediction of protein structural class
EURASIP Journal on Bioinformatics and Systems Biology
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
A Wavelet Tour of Signal Processing, Third Edition: The Sparse Way
Wavelets and filter banks: theory and design
IEEE Transactions on Signal Processing
The discrete wavelet transform: wedding the a trous and Mallatalgorithms
IEEE Transactions on Signal Processing
An overview of statistical learning theory
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
This paper reports a study on static pattern classification technique using Support Vector Machine based Decision Directed Acyclic Graph (DDAG) algorithm for the classification of Malayalam Consonant --- Vowel (CV) speech unit utterances. Wavelet Transform (WT) based Normalized Wavelet Hybrid Features (NWHF) by combining both Classical Wavelet Decomposition (CWD) and Wavelet Packet Decomposition (WPD) along with z --- score normalization are used to evaluate the performance of the present classifier in speaker independent environment. From the experimental study it is reported that present DDAGSVM algorithms perform well for Malayalam CV speech unit recognition compared to ANN and k --- NN in additive noisy condition.