Sleep stages classification is one of the most important methods for diagnosis in psychiatry and neurology. In this paper, a combination of three kinds of classifiers are proposed which classify the EEG signal into five sleep stages including Awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3 and 4 (also called Slow Wave Sleep), and REM. Twenty-five all night recordings...

A new technique presents to improve the performance of dynamic voltage restorer (DVR) for voltage sag mitigation. This control scheme is based on cuckoo search algorithm with tree fuzzy rule based classifier (CSA-TFRC). CSA is used for optimizing the output of TFRC so the classification output of the network is enhanced. While, the combination of cuckoo search algorithm, fuzzy and decision tree...

Quantum machine learning has shown advantages in many ways compared to classical learning. In learning, a difficult problem is how learn model with high robustness and strong generalization ability from limited feature space. Combining multiple models as base learners, ensemble (EL) can effectively improve the accuracy, of final model. The key EL lies two aspects, performance learners choice co...

Target identification from high resolution remote sensing image is a common task for many applications. In order to improve the performance of target identification, multiple classifier combination is used to QuickBird high resolution image, and some key techniques including selection and design of member classifiers, classifier combination algorithm and target identification methods are invest...

In this paper we describe an approach for spoken language analysis for helpdesk call routing using a combination of simple recurrent networks and support vector machines. In particular we examine this approach for its potential in a difficult spoken language classification task based on recorded operator assistance telephone utterances. We explore simple recurrent networks and support vector ma...

Multiple classiier methods are eeective solutions to diicult pattern recognition problems. However, empirical successes and failures have not been completely explained. Amid the excitement and confusion , uncertainty persists in the optimality of method choices for speciic problems due to strong data dependences of classiier performance. In response to this, I propose that further exploration o...

When combining classifiers, we aggregate the output of different machine learning methods, and base our decision on the aggregated probability values instead of the individual ones. In the phoneme classification task of speech recognition, small excerpts of speech need to be identified as one of the pre-defined phonemes; but the probability value assigned to each possible phoneme also hold valu...