A comparative study of DWT, CWT and DCT transformations in ECG arrhythmias classification
Expert Systems with Applications: An International Journal
EEG signal classification using PCA, ICA, LDA and support vector machines
Expert Systems with Applications: An International Journal
Feature extraction using support vector machines
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
Fast support vector training by Newton's method
ICANN'11 Proceedings of the 21st international conference on Artificial neural networks - Volume Part II
Differential electronic nose and support vector machine for fast recognition of tobacco
Expert Systems with Applications: An International Journal
Support vector machine classifier for diagnosis in electrical machines: Application to broken bar
Expert Systems with Applications: An International Journal
Fuzzy machine learning and data mininga
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Training mahalanobis kernels by linear programming
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
A novel method of sparse least squares support vector machines in class empirical feature space
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Combining active learning and semi-supervised learning to construct SVM classifier
Knowledge-Based Systems
Face recognition using Gabor-based direct linear discriminant analysis and support vector machine
Computers and Electrical Engineering
A neuro-fuzzy approach in the classification of students' academic performance
Computational Intelligence and Neuroscience
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A guide on the use of SVMs in pattern classification, including a rigorous performance comparison of classifiers and regressors. The book presents architectures for multiclass classification and function approximation problems, as well as evaluation criteria for classifiers and regressors. Features: Clarifies the characteristics of two-class SVMs; Discusses kernel methods for improving the generalization ability of neural networks and fuzzy systems; Contains ample illustrations and examples; Includes performance evaluation using publicly available data sets; Examines Mahalanobis kernels, empirical feature space, and the effect of model selection by cross-validation; Covers sparse SVMs, learning using privileged information, semi-supervised learning, multiple classifier systems, and multiple kernel learning; Explores incremental training based batch training and active-set training methods, and decomposition techniques for linear programming SVMs; Discusses variable selection for support vector regressors.