Machine Learning
Neural network design
Making large-scale support vector machine learning practical
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Support vector machines based on K-means clustering for real-time business intelligence systems
International Journal of Business Intelligence and Data Mining
Half-Against-Half multi-class support vector machines
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
A combined self-organizing feature map and multilayer perceptronfor isolated word recognition
IEEE Transactions on Signal Processing
Multitraining Support Vector Machine for Image Retrieval
IEEE Transactions on Image Processing
Classifying the Geometric Dilution of Precision of GPS satellites utilizing Bayesian decision theory
Computers and Electrical Engineering
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Support vector machine (SVM) is one of the state-of-the-art tools for linear and non-linear pattern classification. One of the design objectives of an SVM classifier is reducing the number of support vectors without compromising the classification accuracy. For this purpose, a novel technique referred to as diminishing learning (DL) technique is proposed in this paper for a multiclass SVM classifier. In this technique, a sequential classifier is proposed wherein the classes which require stringent boundaries are tested one by one and once the tests for these classes fail, the stringency of the classifier is increasingly relaxed. An automated procedure is also proposed to obtain the optimum classification order for SVM-DL classifier in order to improve the recognition accuracy. The proposed technique is applied for SVM based isolated digit recognition system and is studied using speaker dependent and multispeaker dependent TI46 database of isolated digits. Both LPC and MFCC are used for feature extraction. The features extracted are mapped using self-organized feature maps (SOFM) for dimensionality reduction and the mapped features are used by SVM classifier to evaluate the recognition accuracy using various kernels. The performance of the system using the proposed SVM-DL classifier is compared with those using other techniques: one-against-all (OAA), half-against-half (HAH) and directed acyclic graph (DAG). SVM-DL classifier results in 1-2% increase in recognition accuracy compared to HAH classifier for some of the kernels with both LPC and MFCC feature inputs. For MFCC feature inputs, both HAH and SVM-DL classifiers have 100% recognition accuracy for some of the kernels. The total number of support vectors required is the least for HAH classifier followed by the SVM-DL classifier. The proposed diminishing learning technique is applicable for a number of pattern recognition applications.