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Showing 1 to 20 of 418 for “"Support vector machine"”.
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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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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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A support vector machine embedded weed identification system
… for accomplishing such a control strategy. A machine vision system for weed identification, which utilized the morphological properties of weed leaves, was developed in this research. The system incorporated a new image segmentation algorithm, termed the ‘Pixelwise method’ to binarize the …
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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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Using information theoretic measures to evaluate support vector machine kernels
… can be used to evaluate the kernel of a support vector machine.
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Ensemble Support Vector Machine Models of Radiation-Induced Lung Injury Risk
… existing data are under-sampled and imbalanced. Support vector machines: SVMs), a class of statistical learning methods that implicitly maps data into a higher dimensional space, is one machine learning method that recently has been applied to the RP problem with encouraging results. In this …
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Eye detection using discriminatory features and an efficient support vector machine
… has broad applications in computer vision, machine learning, and pattern recognition. This dissertation presents a number of accurate and efficient eye detection methods using various discriminatory features and a new efficient Support Vector Machine (eSVM). This dissertation first …
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Support Vector Machine Classification of Network Streams Using a Spectrum Kernel Encoding
… network stream classification, relying on the Support Vector Machine (SVM) algorithm. Using only information available in the headers of TCP packets, the SVM creates temporal features – encoded using a spectrum kernel representation – and aggregate features for classification. Experimental …
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Prediction of Severity of Aviation Landing Accidents Using Support Vector Machine Models
<p>The purpose of this study was to apply support vector machine (SVM) models to predict the severity of aircraft damage and the severity of personal injury during an aircraft approach and landing accident and to evaluate and rank the importance of 14 accident factors to the severity. Three new …
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Support Vector Machine-based Fuzzy Systems for Quantitative Prediction of Peptide Binding Affinity
… A non-linear system is proposed with the aid of support vector-based regression to improve the fuzzy system and applied to the real value prediction of degree of peptide binding. This research study introduced two novel methods to improve structure and parameter identification of fuzzy systems. …
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Support vector machine and parametric wavelet-based texture classification of stem cell images
… decomposition, and we employ a non-parametric support vector machine to perform the classification that yields the segmentation. We also adapt a parametric wavelet-based classifier that utilizes the Kullback-Leibler distance. We apply both methods to a set of benchmark textural images, report …
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Explanation-Based Approach to Incorporating Domain Knowledge Into Support Vector Machine: Theory and Applications
… a first step towards a new research area in machine learning.
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Support-vector-machine based automatic performance modelling and optimisation for analogue and mixed-signal designs
… in AMS synthesis systems. Recently, the support vector machine (SVM) method has been introduced into this challenging field. This research has studied the application of SVMs to AMS performance modelling in terms of the computational cost and prediction accuracy. A novel, general …
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Detecting domestic objects with ensembles of view-tuned support vector machine cascades trained on Web images
… View-tuned kernels are used efficiently with Support Vector Machines in a sliding window approach to train view-tuned experts for the different sub-sets. Finally, the outputs of various experts are fused to determine a final detection result. The proposed system shows a state-of-the-art …
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An improved algorithm for iris classification by using support vector machine and binary random machine learning
In machine learning, there are three type of learning branch that can used in classification procedures for data mining. Those branch are consist of supervised learning, unsupervised learning and reinforcement learning. This study focuses on supervised learning that seek to classify all the Iris …
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