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Showing 1 to 20 of 709 for “"support vector"”.
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Techniques in support vector classification
… Rn and X, Y ⊂ Rn we can solve this problem using Support Vector Machines. Support Vector Machines are functions of the form ƒ(z) = sign (∑i αik(xi, z) + ∑jβjk(yj, z) + b), (*) where k : Rn x Rn → R and z is classified as a member of X = {xi} if ƒ(z) > 0 and a member of Y = {yj} otherwise. We …
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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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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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Clustering Via Supervised Support Vector Machines
… SVM classifier against a data set with each vector in the set randomly labeled. Once this initialization step is complete, the SVM confidence parameters for classification on each of the training instances can be accessed. The lowest confidence data (e.g., the worst of the mislabeled data) …
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Evolutionary Optimization Of Support Vector Machines
Support vector machines are a relatively new approach for creating classifiers that have become increasingly popular in the machine learning community. They present several advantages over other methods like neural networks in areas like training speed, convergence, complexity control of the …
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Support vector machines : training and applications
Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, 1998.
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Structural Damage Classification using Support Vector Machines
… using a time-frequency representation method and support vector machines is investigated. Piezoelectric ceramic actuators are utilized to generate guided wave signals on a set of aluminum beam coupons with different damage features, such as types, locations, and thicknesses. The short-time Fourier …
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Support Vector Machine algorithms : analysis and applications
Support Vector Machines (SVMs) have attracted recent attention as a learning technique to attack classification problems. The goal of my thesis work is to improve computational algorithms as well as the mathematical understanding of SVMs, so that they can be easily applied to real problems. SVMs …
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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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Classification under input uncertainty with support vector machines
… incorporate the known input uncertainties into support vector machines (SVMs), which can accommodate isotropic uncertain information in the classification. This new method is termed as uncertainty support vector classification (USVC). Kernel functions can be used as well through the derivation …
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Classifying RNA secondary structures using support vector machines
… them in a reduced dimensional space using Support Vector Machines.
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A support vector machine embedded weed identification system
… feature extraction procedures. Subsequently, a Support Vector Machine (SVM) based classifier was constructed to distinguish various weed species using seven morphological features. 2,325 indoor images consisting of six weed species were acquired during the first five weeks after emergence of the …
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Vehicle Lane Departure Prediction Based On Support Vector Machines
… we explored utilizing the nonlinear binary support vector machine (SVM) technique and the time series of vehicle variables to predict unintentional lane departure, which is innovative as no machine learning technique has previously been attempted for this purpose in the literature. …
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Image segmentation and pattern classification using support vector machines
… method in input and feature spaces using Support Vector Machines (SVMs) is developed. In the input space, a subset of input features is selected by the ranking of their contributions to the decision function. In the feature space, features are ranked according to the weighted support …
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Support vector machines, N-gram kernels, and text classification
… In recent years, a new inference method known as Support Vector Machines (SVMs) has been increasingly applied to the task of text classification. The results have been promising and research shows that they outperform several conventional methods. One the key components of SVMs are kernel …
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Rule Extraction from Support Vector Machines: A Geometric Approach
… and present limited generalization performance. Support Vector Machine is an unsupervised learning method that has been recently applied successfully in many areas, and o®ers excellent generalization ability in comparison with other neural network, statistical, or symbolic machine learning …
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Support vector machine and its applications in information processing
… complexity of hidden patterns is the support vector machine. The support vector machine has been developed as robust tool for classification and regression in noisy, complex domains. Current thesis work is aimed to explore the area of support vector machine to see the interesting …
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RF channel characterization for cognitive radio using support vector machines
… (DFT) and their kernel versions, 2.) Linear Support Vector Machines (SVMs) and their kernel versions, and 3.) Neural Networks and/or Genetic Algorithms. Before deciding on what to transmit, a Cognitive Radio must decide where the white space is located. This research is focused on the task of …
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Structured support vector machines learning and application in computer vision
… to a more gen{u00AD} eral task, the structured Support Vector Regression (SVR). Beside the unary features which are adopted in traditional SVR algorithms, the objective function in our framework considers both label information and pairwise features, helping to achieve better cross-smoothing …
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