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The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance...
The document similarity measure is a key point in textual data processing. It is the main responsible of the performance of a processing system. Since a decade, kernels are used as similarity functions within inner-product based algorithms such as the SVM for NLP problems and especially for text categorization. In this paper, we present a semantic space constructed from latent concepts. The concepts...
This paper presents a new face recognition approach by using correlation analysis and ensemble classifiers based on support vector machine (SVM). In this approach, image pre-processing techniques such as histogram equalization, edge detection and geometrical transformation are first used in order to improve the quality of the face images. We further employ correlation analysis method to extract features...
In this work we use support vector machine to predict polyadenylation sites (Poly (A) sites) in human DNA and mRNA sequences by analyzing features around them. Two models are created. The first model identifies the possible location of the Poly (A) site effectively. The second model distinguishes between true and false Poly (A) sites, hence effectively detect the region where Poly (A) sites and transcription...
Reduction of feature dimensionality is of considerable importance in machine learning. The generalization performance of classification system improves when correlated and redundant features are removed. In order to reduce the dimensionality of pattern representation, A new feature election method for support vector machine is proposed. Based on pattern similarity measurement in kernel space, lass...
In this paper, we implement a new method for classification of biological signals in general, and use it in the animal behavior classification as an example. The forced swimming test of rats or mice is a frequently used behavioral test to evaluate the efficacy of drugs in rats or mice. Frequently used features for that evaluation are obtained through observing three states: immobility, struggling/climbing...
In this work we show that a metaheuristic, the variable neighborhood search (VNS), can be effectively used in order to improve the performance of the hardware-friendly version of the support vector machine (SVM). Our target is the implementation of the feed-forward phase of SVM on resource-limited hardware devices, such as field programmable gate arrays (FPGAs) and digital signal processors (DSPs)...
Document representation is one of the crucial components that determine the effectiveness of text classification tasks. Traditional document representation approaches typically adopt a popular bag-of-word method as the underlying document representation. Although itpsilas a simple and efficient method, the major shortcoming of bag-of-word representation is in the independent of word feature assumption...
One of the Internetpsilas hallmark is the rapid spread of the use of information and communication technology. This has boosted methods for hiding stego information inside digital cover content images which is a concerning issue in information security. On the other hand, attack of steganographic schemes has leveraged methods for steganalysis which is a challenging problem. In this paper, first we...
Jet engine gas path fault diagnosis is not only important in modern condition-based maintenance of aircraft engines, but also a challenging classification problem. Exploring more advanced classification techniques for achieving improved classification performance for gas path fault diagnosis, therefore, has been increasingly active in recent years in PHM community. To that end, in this paper, we apply...
The increasing demands for homeland security boost the development of an intelligent recognition system for through-the-wall sensing. A novel intelligent through-the-wall life recognition engine based on support vector machine (SVM) is provided herein. In this system, micro-Doppler signatures detected from through-the-wall radar are extracted and fed into a SVM classifier. Micro-Doppler effect has...
As advances in green automotives, hybrid electric vehicle (BEV) has being given more and more attention in recent years. The power management control strategy of BEV is the key problem that determines the efficiency and pollution emission level of the BEV, which requires the forecast of driving load situation of BEV in advance. This paper proposes an efficient approach for forecasting the driving...
Analyzing the principle of typical financial crisis early-warning model, this study summarizes the limitations of them and their requirement of variance. An empirical research is carried out on how to sample the Chinese listed companies of A-stock market in Shanghai and Shenzhen, and how to determine the core parameters of support vector machine (SVM) as well. This research also studies the predicting...
We propose a feature selection criterion based on kernel discriminant analysis (KDA) for a n-class problem, which finds eigenvectors on which the projected class data are locally maximally separated. The proposed criterion is the sum of the objective function values of KDA associated with the n-1 eigenvectors. The criterion results in calculating the sum of n-1 eigenvalues associated with the eigenvectors...
This paper presents an investigation of the influence of the RePART (Reward and Punishment ARTmap) neural network in structures of ensembles designed by three variants of boosting: Aggressive, Conservative and Inverse Boosting. In this investigation, it is aimed to analyze whether the use of this model is positive for ARTMAP-based ensembles. In addition, it aims to define which boosting strategy is...
In this paper, we propose several active learning strategies to train classifiers for phosphorylation site prediction. When combined with support vector machine, we show that active learning with SVM is able to produce classifiers that give comparable or better phosphorylation site prediction performance than conventional SVM techniques and, at the same time, require a significantly less number of...
Kernel ridge regression (KRR) is a nonlinear extension of the ridge regression. The performance of the KRR depends on its hyperparameters such as a penalty factor C, and RBF kernel parameter sigma. We employ a method called MCV-KRR which optimizes the KRR hyperparameters so that a cross-validation error is minimized. This method becomes equivalent to a predictive approach to Gaussian process. Since...
In Support Vector Regression (SVR), kernel functions are used to deal with nonlinear problem by computing the inner product in a higher dimensional feature space. The performance of approximation depends on the chosen kernels. Although the radial basis function (RBF) kernel has been successfully used in many problems, it still has the restriction in some complex problems. In order to obtain a more...
Video scene classification and segmentation are fundamental steps for multimedia retrieval, indexing and browsing. In this paper, a robust scene classification and segmentation approach based on support vector machine (SVM) is presented, which extracts both audio and visual features and analyzes their inter-relations to identify and classify video scenes. Our system works on content from a diverse...
In many traffic sign recognition system, one of the main tasks is to classify the shapes of traffic sign. In this paper, we have developed a shape-based classification model by using support vector machines. We focused on recognizing seven categories of traffic sign shapes and five categories of speed limit signs. Two kinds of features, binary image and Zernike moments, were used for representing...
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