The nature of statistical learning theory
The nature of statistical learning theory
Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
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)
EEG signal classification using wavelet feature extraction and a mixture of expert model
Expert Systems with Applications: An International Journal
Engineering Applications of Artificial Intelligence
Recurrent neural networks employing Lyapunov exponents for EEG signals classification
Expert Systems with Applications: An International Journal
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Classification of electroencephalogram signals with combined time and frequency features
Expert Systems with Applications: An International Journal
Clustering technique-based least square support vector machine for EEG signal classification
Computer Methods and Programs in Biomedicine
A tunable support vector machine assembly classifier for epileptic seizure detection
Expert Systems with Applications: An International Journal
Proceedings of the International Conference on Advances in Computing, Communications and Informatics
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Identification of motor imagery tasks through CC-LR algorithm in brain computer interface
International Journal of Bioinformatics Research and Applications
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
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Over the last few decades pattern classification has been one of the most challenging area of research. In the present-age pattern classification problems, the support vector machines (SVMs) have been extensively adopted as machine learning tools. SVM achieves higher generalization performance, as it utilizes an induction principle called structural risk minimization (SRM) principle. The SRM principle seeks to minimize the upper bound of the generalization error consisting of the sum of the training error and a confidence interval. SVMs are basically designed for binary classification problems and employs supervised learning to find the optimal separating hyperplane between the two classes of data. The main objective of this paper is to introduce a most promising pattern recognition technique called cross-correlation aided SVM based classifier. The idea of using cross-correlation for feature extraction is relatively new in the domain of pattern recognition. In this paper, the proposed technique has been utilized for binary classification of EEG signals. The binary classifiers employ suitable features extracted from crosscorrelograms of EEG signals. These cross-correlation aided SVM classifiers have been employed for some benchmark EEG signals and the proposed method could achieve classification accuracy as high as 95.96% compared to a recently proposed method where the reported accuracy was 94.5%.