Training Data For Support Vector Machine Research Articles

An Enhancement of Svm Based Semantically Enriched Variable Length Confidence Pruned Markov Chain Model Based Web Page Recommendation System

Semantic Variable Length Markov Chain Length Model (SVLMC) is a web page recommendation system which combined the fields of semantic web and web usage mining by the Markov transition probability matrix with rich semantic information extracted from web pages. Though it has high prediction accuracy, it has problem of high state space complexity. The high space complexity reduce the execution speed and reduce the performance of the system, which was resolved by Semantic Variable Length confidence pruned Markov Chain Model (SVLCPMC) model that provides high user satisfied recommendation and Confidence Pruned Markov Model (CPMM). The time consumption of CPMM was reduced by Support Vector Machine (SVM). But still the recommendation accuracy is still below the user satisfaction. So in this paper, quickest change detection using Kullback-Leibler Divergence method is introduced to improve the accuracy of recommendation generation by developing a scalable quickest change detection schemes that can be implemented recursively in a more complicated scenario of Markov model and it is included in the training data of SVM. Then the performance of web page recommendation is improved by ranking the web pages using page ranking technique. Thus the performance of web page recommendation generation system has been improved. The experiments are conducted to prove the effectiveness of the proposed work in terms of prediction accuracy, precision, recall, F1-measure, coverage and R measure.

Recognition of genetically modified product based on affinity propagation clustering and terahertz spectroscopy

In this paper, based on the terahertz spectrum, a new identification method of genetically modified material by support vector machine (SVM) based on affinity propagation clustering is proposed. This algorithm mainly uses affinity propagation clustering algorithm to make cluster analysis and labeling on unlabeled training samples, and in the iterative process, the existing SVM training data are continuously updated, when establishing the identification model, it does not need to manually label the training samples, thus, the error caused by the human labeled samples is reduced, and the identification accuracy of the model is greatly improved.

Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs

Support vector machines (SVMs) are one of the most popular and powerful machine learning techniques, but suffer from a significant drawback of the high time and memory complexities of their training. This issue needs to be endured especially in the case of large and noisy datasets. In this paper, we propose a new adaptive memetic algorithm (PCA2MA) for selecting valuable SVM training data from the entire set. It helps improve the classifier score, and speeds up the classification process by decreasing the number of support vectors. In PCA2MA, a population of reduced training sets undergoes the evolution, which is complemented by the refinement procedures. We propose to exploit both a priori information about the training set—extracted using the data geometry analysis—and the knowledge attained dynamically during the PCA2MA execution to enhance the refined sets. Also, we introduce a new adaptation scheme to control the pivotal algorithm parameters on the fly, based on the current search state. Extensive experimental study performed on benchmark, real-world, and artificial datasets clearly confirms the efficacy and convergence capabilities of the proposed approach. We demonstrate that PCA2MA is highly competitive compared with other state-of-the-art techniques.

Principal pixel analysis and SVM for automatic image segmentation

Segmenting objects from images is an important but highly challenging problem in computer vision and image processing. This paper presents an automatic object segmentation approach based on principal pixel analysis (PPA) and support vector machine (SVM), namely PPA---SVM. The method comprises three main steps: salient region extraction, principal pixel analysis, as well as SVM training and segmentation. We consider global saliency information and color feature by means of visual saliency detection and histogram analysis, such that SVM training data can be selected automatically. Experiment results on a public benchmark dataset demonstrate that, compared with some classical segmentation algorithms, the proposed PPA---SVM method can effectively segment the whole salient object with reasonable better performance and faster speed.

Measurement of Micro Vibration of Car by Piezoelectric Ceramics -Detection of Bio-Informations in the Car and Application for Security-

Recently, there are various accidents and crimes related to the car. In some cases, the accidents and the crimes can be prevented if it is possible to detect a human who is in the car. For example, we can prevent a baby who is left in a car under the hot weather from dehydration or death occurred by heat inside disease. In another case, it is estimated that the United States currently has as many as 12 million illegal immigrants. In order to prevent further influx of illegal immigrants, the police are physically searching incoming vehicles at national boundaries aiming at finding those who are hiding inside. However, the physical inspections require much manpower cost and time. An inspection method to see inside the vehicles through X-ray images has also been used for this end. But the cost and the installation places are the problems of the large-scale X-ray system. Proposed in this paper is a piezoelectric ceramic system to handily measure the micro vibrations of motor vehicles. And applying the algorithm of Support Vector Machine (SVM), the existence of human body inside vehicles can be detected. The experiment was carried out using four types of vehicles: a mini car; an auto mobile; a van; and a truck weighing 1.5 tons. As the results, the correct determination ratio was 91.2% for the experiment with the piezoelectric ceramic under the front wheels and 97.0% under the rear wheels, when the vehicle used for the examination had also been used together with other three types of vehicles to obtain SVM training data. When the vehicle used for the examination had not been used together with the other three to obtain SVM training data, on the other hand, the correct determination ratio was 93.7% for the experiment with the piezoelectric ceramic under the front wheels and 95.7% under the rear wheels.

Clustering-Based Extraction of Border Training Patterns for Accurate SVM Classification of Hyperspectral Images

This letter presents an accurate support vector machine (SVM)-based hyperspectral image classification algorithm, which uses border training patterns that are close to the separating hyperplane. Border training patterns are obtained in two consecutive steps. In the first step, clustering is performed to training data of each class, and cluster centers are taken as initial training data for SVM. In the second step, the reduced-size training data composed of cluster centers are used in SVM training, and cluster centers obtained as support vectors at this step are regarded to be located close to the hyperplane border. Original training samples are contained in clusters for which the cluster centers are obtained to be close to the hyperplane border and the corresponding cluster centers are then together assigned as border training patterns. These border training patterns are then used in the training of the SVM classifier. Experimental results show that it is possible to significantly increase the classification accuracy of SVM using border training patterns obtained with the proposed approach.

Loop Length Dependent SVM Prediction of Domain Linkers

The practical importance of the prediction of structural domains in un-annotated amino acid sequences has increased as they represent valuable targets readily characterized by high throughput methods. Here we report a support vector machine (SVM) prediction of domain linkers, which are loop regions separating two structural domains. The SVM training data set comprised 182 protein sequences from SCOP database, which contained at least one domain linker regions (all). Furthermore, the data set was divided into long (longer than 9 residues) and short (shorter than or equal to 9 residues) linker sequences. Using these data sets, we constructed three loop length dependent SVMs (SVM-All, SVM-Long, and SVM-Short), which were trained using all, long and short linkers, respectively. In addition, our new SVM input data used a position specific scoring matrix (PSSM) and predicted secondary structure information (PSS). A five-fold cross validation test indicates that the area under the ROC (receiver operating characteristics) curve (AUC) value, which represents the prediction performance, of SVM-All, SVM-Long and SVM-Short were 0.763, 0.759 and 0.759, respectively. Our previous SVMs, which used only amino acid sequence information, indicated prediction performances of 0.692, 0.702, and 0.605, for SVM-All, SVM-Long and SVM-Short respectively. The prediction performances of our new predictors thus were over 10% higher than those of our previous methods, Armadillo ( 1) (AUC value: 0.610 Dumontier et. al. J Mol Biol 2005), and neural network based method (2)(AUC value: 0.642 Miyazaki et. al. BMC Bioinformatics 2006). These results demonstrate the efficiency of our new methods. Thus, the performance has been improved by the inclusion of PSSM and PSS, in addition to sequences.