Search for: combining-classifiers
M.Sc. Thesis Sharif University of Technology ;
Classification is one the contrivesial problems in machine vision and pattern recongnition. Traditional feature extraction methods which are based on low level feature extraction do not have high classification accuracy, thus they do not have the ability to represent images in feature space in discriminative way. In this thesis we have proposed a grid base method and used hidden Markov model (HMM) to include topological and spatial information in feature vectors. Then the classifiers created based on HMM feature extraction are combind. Combination of classifiers is based on designing a convex goal function. The goal of this optimization is to determine the wight of each classifier for...
Article 2011 International Symposium on Artificial Intelligence and Signal Processing, AISP 2011, 15 June 2011 through 16 June 2011 ; June , 2011 , Pages 31-36 ; 9781424498345 (ISBN) ; Beigy, H ; Sharif University of Technology
The voluminous of the e-mails are spam. Several algorithms are represented for spam detection based on batch learning. In this paper, a new algorithm based on incremental learning is introduced. The algorithm composes new knowledge from new training data with previous knowledge by combining classifiers based on weighted majority voting. The experiment results show that the proposed algorithm outperforms other related incremental algorithms and non-incremental algorithms
Article World Academy of Science, Engineering and Technology ; Volume 70 , 2010 , Pages 788-794 ; 2010376X (ISSN) ; Zakernejad, S ; Faridi, S ; Javadi, M ; Ebrahimpour, R ; Sharif University of Technology
This paper illustrates the use of a combined neural network model for classification of electrocardiogram (ECG) beats. We present a trainable neural network ensemble approach to develop customized electrocardiogram beat classifier in an effort to further improve the performance of ECG processing and to offer individualized health care. We process a three stage technique for detection of premature ventricular contraction (PVC) from normal beats and other heart diseases. This method includes a denoising, a feature extraction and a classification. At first we investigate the application of stationary wavelet transform (SWT) for noise reduction of the electrocardiogram (ECG) signals. Then...