Title :
Support Vector Machine Classifier for Pattern Recognition
Author :
Farhan, Mohammad ; Kassem, Ghulam ; Abdullah, Mujeeb ; Akbar, Siddique
Author_Institution :
Inst. of Eng. & Comput. Sci., Univ. of Sci. & Technol., Bannu, Pakistan
Abstract :
Automatiuc speech recognition is carried out by Mel-frequency cepstral coefficient (MFCC). Linearly-spaced at low and logarithmic-spaced filters at higher frequencies are used to capture the characteristics of speech. Multi-layer perceptrons (MLP) approximate continuous and non-linear functions. High dimensional patterns are not permitted due to eigen-decomposition in high dimensional image space and degeneration of scattering matrices in small size sample. Generalization, dimensionality reduction and maximizing the margins are controlled by minimizing weight vectors. Results show good pattern by SVM algorithm with Mercer kernel.
Keywords :
eigenvalues and eigenfunctions; matrix algebra; multilayer perceptrons; pattern classification; speech recognition; support vector machines; Mercer kernel; automatic speech recognition; dimensionality reduction; eigendecomposition; high dimensional image space; logarithmic spaced filters; mel frequency cepstral coefficient; multilayer perceptrons; pattern recognition; scattering matrices; support vector machine classifier; Covariance matrix; Feature extraction; Kernel; Mel frequency cepstral coefficient; Speech; Support vector machines; Vectors; Linear Discriminant Analysis (LDA); Mel-frequency Cepstral Coefficient (MFCC); Mercer kernel; Support Vector Machine (SVM); k-Nearest Neighbor (kNN) classifier; kernel;
Conference_Titel :
Informatics and Computational Intelligence (ICI), 2011 First International Conference on
Conference_Location :
Bandung
Print_ISBN :
978-1-4673-0091-9
DOI :
10.1109/ICI.2011.52
