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Search Results: 1 - 10 of 1139 matches for " SVM "
All listed articles are free for downloading (OA Articles)
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Text Classification Using Support Vector Machine with Mixture of Kernel  [PDF]
Liwei Wei, Bo Wei, Bin Wang
Journal of Software Engineering and Applications (JSEA) , 2012, DOI: 10.4236/jsea.2012.512B012
Abstract: Recent studies have revealed that emerging modern machine learning techniques are advantageous to statistical models for text classification, such as SVM. In this study, we discuss the applications of the support vector machine with mixture of kernel (SVM-MK) to design a text classification system. Differing from the standard SVM, the SVM-MK uses the 1-norm based object function and adopts the convex combinations of single feature basic kernels. Only a linear programming problem needs to be resolved and it greatly reduces the computational costs. More important, it is a transparent model and the optimal feature subset can be obtained automatically. A real Chinese corpus from FudanUniversityis used to demonstrate the good performance of the SVM- MK.
Learning Actions from the Identity in the Web  [PDF]
Khawla Hussein Ali, Tianjiang Wang
Journal of Computer and Communications (JCC) , 2014, DOI: 10.4236/jcc.2014.29008
Abstract:

This paper proposes an efficient and simple method for identity recognition in uncontrolled videos. The idea is to use images collected from the web to learn representations of actions related with identity, use this knowledge to automatically annotate identity in videos. Our approach is unsupervised where it can identify the identity of human in the video like YouTube directly through the knowledge of his actions. Its benefits are two-fold: 1) we can improve retrieval of identity images, and 2) we can collect a database of action poses related with identity, which can then be used in tagging videos. We present the simple experimental evidence that using action images related with identity collected from the web, annotating identity is possible.

Classification Methods of Skin Burn Images
Malini Suvarna,Sivakumar,U C Niranjan
International Journal of Computer Science & Information Technology , 2013,
Abstract: In this paper,methodsto automatically detect and categorize the severity of skin burn imagesusingvariousclassification techniquesare compared andpresented. A database comprisingofskin burn imagesbelonging to patients of diverseethnicity, genderand age areconsidered. First the images arepreprocessed andthen classifiedutilizingthe pattern recognitiontechniques:TemplateMatching(TM),Knearestneighbor classifier (kNN) and Support Vector Machine (SVM).The classifier istrained fordifferentskin burn grades using pre-labeled images and optimizedfor the features chosen. This algorithmdeveloped,works as an automatic skin burn wound analyzerandaids in the diagnosisof burn victims
Using Least Squares Support Vector Machines for Frequency Estimation  [PDF]
Xiaoyun Teng, Xiaoyi Zhang, Hongyi Yu
Int'l J. of Communications, Network and System Sciences (IJCNS) , 2010, DOI: 10.4236/ijcns.2010.310111
Abstract: Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently without the need of statistics knowledge of the observations, and the estimation performance is insensitive to the carrier phase. Simulation results are presented showing that proposed estimators offer better performance than traditional Maximum Likelihood (ML) estimator at low SNR, since classification-based method does not have the threshold effect of nonlinear estimation.
Comparison of SVM and ANN for classification of eye events in EEG  [PDF]
Rajesh Singla, Brijil Chambayil, Arun Khosla, Jayashree Santosh
Journal of Biomedical Science and Engineering (JBiSE) , 2011, DOI: 10.4236/jbise.2011.41008
Abstract: The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can con-trol his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector ma-chines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multi-layer ANN is trained to detect the eye events. A com-parison of both is made in this paper.
Prediction of human microRNA hairpins using only positive sample learning  [PDF]
Dang Hung Tran, Tho Hoan Pham, Kenji Satou, Tu Bao Ho
Journal of Biomedical Science and Engineering (JBiSE) , 2008, DOI: 10.4236/jbise.2008.12023
Abstract: MicroRNAs(miRNA) are small molecular non-coding RNAs that have important roles in the post-transcriptional mechanism of animal and plant. They are commonly 21-25 nucleotides (nt) long and derived from 60-90 nt RNA hairpin structures, called miRNA hairpins. A larger num-ber of sequence segments in the human genome have been computationally identified with such 60-90 nt hairpins, however a majority of them are not miRNA hairpins. Most computational meth-ods so far for predicting miRNA hairpins were based on a two-class classifier to distinguish between miRNA hairpins and other sequence segments with hairpin structures. The difficulty of these methods is how to select hairpins as negative examples of miRNA hairpins in the classifier-training datasets, since only a few miRNA hairpins are available. Therefore, their classifier may be mis-trained due to some false negative examples of the training dataset. In this paper, we introduce a one-class support vector machine (SVM) method to predict miRNA hair-pins among the hairpin structures. Different from existing methods for predicting miRNA hairpins, the one-class SVM model is trained only on the information of the miRNA class. We also illus-trate some examples of predicting miRNA hair-pins in human chromosomes 10, 15, and 21, where our method overcomes the above disad-vantages of existing two-class methods.
Electroencephalography Analysis Using Neural Network and Support Vector Machine during Sleep  [PDF]
JeeEun Lee, Sun K. Yoo
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.55B018
Abstract: The purpose of this paper is to analyze sleep stages accurately using fast and simple classifiers based on the frequency domain of electroencephalography(EEG) signal. To compare and evaluate system performance, the rules of Rechtschaffen and Kales(R&K rule) were used. Parameters were extracted from preprocessing process of EEG signal as feature vectors of each sleep stage analysis system through representatives of back propagation algorithm and support vector machine (SVM). As a result, SVM showed better performance as pattern recognition system for classification of sleep stages. It was found that easier analysis of sleep stage was possible using such simple system. Since accurate estimation of sleep state is possible through combination of algorithms, we could see the potential for the classifier to be used for sleep analysis system.
Prediction of Peptides Binding to Major Histocompatibility Class II Molecules Using Machine Learning Methods  [PDF]
Fateme Kazemi Faramarzi, Majid Mohammad Beigi, Yasamin Botorabi, Najme Mousavi
Engineering (ENG) , 2013, DOI: 10.4236/eng.2013.510B105
Abstract:

In daily life,we are frequently attacked by infection organisms such as bacteria and viruses. Major Histocompatibility (MHC) molecules have an essential role in T-cell activation and initiating an adaptive immune response. Development of methods for prediction of MHC-Peptide binding is important in vaccine design and immunotherapy. In this study, we try to predict the binding between peptides and MHC class II. Support vector machine (SVM) and Multi-Layer Percep-tron (MLP) are used for classification. These classifiers based on pseudo amino acid compositions of data that we ex-tracted from PseAAC server, classify the data. Since, the dataset, used in this work, is imbalanced, we apply a pre-processing step to over-sample the minority class and come over this problem. The results show that using the concept of pseudo amino acid composition and applying over-sampling method, increases the performance of predictor. Fur-thermore, the results demonstrate that using the concept of PseAAC and SVM is a successful method for the prediction of MHC class II molecules.

Stock Market Prediction Using Heat of Related Keywords on Micro Blog  [PDF]
Shengchen Zhou, Xunzhi Shi, Yunchen Sun, Wenting Qu, Yingzi Shi
Journal of Software Engineering and Applications (JSEA) , 2013, DOI: 10.4236/jsea.2013.63B009
Abstract: Whether the stock market investors’ emotion can influence the stock market itself is one of the hot topic in financial research. In this paper, a method based on the heat of related keywords on Micro Blog is proposed, as Micro Blog is an ideal source for capturing public opinions towards certain topic. We choose Shanghai Composite index as the research object, through correlation analysis, Granger causality analysis, and support vector machine classification, the results have shown that the keywords heat on micro blog can make a short-time prediction of stock market, and the keyword which expresses negative emotion have more powerful prediction ability.
Robust Spatial Filters on Three-Class Motor Imagery EEG Data Using Independent Component Analysis  [PDF]
Bangyan Zhou, Xiaopei Wu, Lei Zhang, Zhao Lv, Xiaojing Guo
Journal of Biosciences and Medicines (JBM) , 2014, DOI: 10.4236/jbm.2014.22007
Abstract:

Independent Component Analysis (ICA) was often used to separate movement related independent components (MRICs) from Electroencephalogram (EEG) data. However, to obtain robust spatial filters, complex characteristic features, which were manually selected in most cases, have been commonly used. This study proposed a new simple algorithm to extract MRICs automatically, which just utilized the spatial distribution pattern of ICs. The main goal of this study was to show the relationship between spatial filters performance and designing samples. The EEG data which contain mixed brain states (preparing, motor imagery and rest) were used to design spatial filters. Meanwhile, the single class data was also used to calculate spatial filters to assess whether the MRICs extracted on different class motor imagery spatial filters are similar. Furthermore, the spatial filters constructed on one subject’s EEG data were applied to extract the others’ MRICs. Finally, the different spatial filters were then applied to single-trial EEG to extract MRICs, and Support Vector Machine (SVM) classifiers were used to discriminate left handright-hand and foot imagery movements of BCI Competition IV Dataset 2a, which recorded four motor imagery data of nine subjects. The results suggested that any segment of finite motor imagery EEG samples could be used to design ICA spatial filters, and the extracted MRICs are consistent if the position of electrodes are the same, which confirmed the robustness and practicality of ICA used in the motor imagery Brain Computer Interfaces (MI-BCI) systems.

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