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Showing 1 to 20 of 459 for “"SVM"”.
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Offline and online SVM performance analysis
… and weaknesses between the offline and online SVM. The work includes the performance comparisons of SVMLight and LaSVM, with results of training time, number of support vectors, kernel evaluations, and test accuracies. Multiple datasets are experimented to cover a wide range of input data and …
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SVM-based Strategies as applied to Electromagnetics
… problems by means of support vector machine (SVM). The proposed instance-based classification method compared to more traditional optimization techniques solves the arising quadratic optimization problem with constraints in a simple and reliable way leveraging on the statistical learning …
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Undersampling GA-SVM for network intrusion detection
… Genetic Algorithm-Support Vector Machine (GA-SVM) method to handle this problem. It applies an undersampling method in GA-SVM. To solve the multiclassification problem with a binary classifier, this work proposes to utilize the undersampling GA-SVM with several classic structures. After …
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Enhanced Weak Signal Detection Using SVM Based Correlation Algorithm
Traditional signal detection algorithms are often robust and are typically sufficient for high SNR data. However, the assumptions behind these methods begin to fall apart when signal period becomes either very short, or small in amplitude compared to any corruptive noise. To address this a kernel …
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Evaluation of different Support Vector Machines (SVM) for speaker identification
… investigation into four support vector machines (SVM) kernels. SVMs have gained much acceptance in classification tasks since their inception in the 1990s. The central feature of SVM is the implicit mapping of input data to some higher-dimensional feature space. This is achieved through the use of …
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Land cover mapping through optimizing remote sensing data for SVM classification
Support Vector Machines (SVMs) are a new supervised classification technique that has its roots in statistical learning theory. It has gained popularity in fields such as machine vision, artificial intelligence, digital image processing and more recently remote sensing. The three commonly used SVMs …
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Feature extraction and normalization in SVM speaker verification using telephone speech
… NIST 2000 Speaker Recognition Evaluation. The SVM is a discriminative classifier with good generalization characteristics. It has been shown to perform as well as, and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to …
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SVM-based Harris Corner Detection to Classify Normal/Abnormal Breast Mammogram Images
… training data set for Support Vector Machine (SVM) to classify a breast mammogram image as normal or abnormal. Corner pixels from improved Harris Corner Detection are used as a training input feature for SVM. The experimental results demonstrate that the proposed method achieved higher accuracy …
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Detection of mental task related EEG for brain computer interface implementation (using SVM classification approach)
… transform and support vector machine (FFT-SVM) to the problem of mental task detection in EEG-based brain computer interface implementation.
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Distributed Support Vector Machine With Graphics Processing Units
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. Sequential Minimal Optimization (SMO) is a decomposition-based algorithm which breaks this large QP problem into a series of smallest possible QP problems. However, it …
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Improved relative discriminative criterion using rare and informative terms and ringed seal search-support vector machine techniques for text classification
… Ringed Seal Search-Support Vector Machine (RSS-SVM) to improve the generalization and learning capability of the SVM. The proposed RSS-SVM optimizes kernel and penalty parameter with the help of RSS algorithm. The proposed RSS-SVM is compared to the most recent techniques GA-SVM and CS-SVM. The …
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External Support Vector Machine Clustering
The external-Support Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data …
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Distributionally robust binary classifier under Wasserstein distance
… When focusing on the support vector machine (SVM), the general problem boils down to an easy-to-solve second- order cone programming problem. The robustified SVM is then applied to synthetic data with and without contamination, and our simulation studies show that our robustified SVM model can …
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Intelligent Road Control System Using Advanced Image Processing Techniques
Over the past few years, Support Vector Machine (SVM) has been widely used in data classification field and has already been proved as an optimal solution for both linear and nonlinear classification problems. Since the image segmentation can be considered as a type of classification, SVM can be …
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Clustering Via Supervised Support Vector Machines
An SVM-based clustering algorithm is introduced that clusters data with no a priori knowledge of input classes. The algorithm initializes by first running a binary SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM …
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Rule Extraction from Support Vector Machines: A Geometric Approach
… models from Support Vector Machines (SVM). This algorithm views the extraction as a geometric task. Given a trained SVM network, GOSE queries the synthetic instances and draws conjunction rules by approximating the optimization problem. The extracted rule set also represents the …
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Local Approaches for Fast, Scalable and Accurate Learning with Kernels
… analysis and empirical evaluation of the Local SVM approach, (ii) a family of operators on kernels to obtain Quasi-Local kernels, (iii) the framework of Local Kernel Machines, and (iv) a local maximal margin approach to noise reduction. In our analysis of Local SVM, we derive a new learning …
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Distributed Support Vector Machine Learning
Support Vector Machines (SVMs) are used for a growing number of applications. A fundamental constraint on SVM learning is the management of the training set. This is because the order of computations goes as the square of the size of the training set. Typically, training sets of 1000 (500 positives …
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An Evaluation of Text Classification Methods for Literary Study
… such as Odds Ratio does not improve SVM performance and stop word removal might harm classification. Some conclusions contradict previous results, such as SVM does not beat naive Bayes in both cases. Some findings are new to this area---SVM and naive Bayes select top features in …
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