Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 253 for “"support vector machines"”.
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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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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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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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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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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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Data Mining via Support Vector Machines: Scalability, Applicability, and Interpretability
… are based on a principled methodology, i.e., Support Vector Machines (SVMs), to produce higher quality results with less human intervention. We first address several challenges in adopting SVM technology to the practice of data mining: (1) scalability: SVMs are unscalable to data size while …
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Evaluation of different Support Vector Machines (SVM) for speaker identification
This study is an 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 …
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An evaluation of support vector machines in consumer credit analysis
This thesis examines a support vector machine approach for determining consumer credit. The support vector machine using a radial basis function (RBF) kernel is compared to a previous implementation of a decision tree machine learning model. The dataset used for evaluation was provided by a large …
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Performance Analysis of Parallel Support Vector Machines on a MapReduce Architecture
… issues when applied to real world datasets. Support Vector Machines (SVM) are powerful classification and regression tools but their computational requirements increase rapidly as the number of training examples increases. To address this problem, several parallel MapReduce based …
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From supervised to unsupervised support vector machines and applications in astronomy
… anderem die sogenannten halb- und unüberwachten Support Vektor Maschinen vorgestellt; beide Erweiterungen führen jedoch zu schwierigen kombinatorischen Optimierungsproblemen. Die Entwicklung von Optimierungsansätzen für beide Erweiterungen ist eines der zentralen Themen der Dissertation. Über …
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Protein-dependent prediction of messenger RNA binding using Support Vector Machines
… thesis addresses the creation of models based on support vector machines and trained on experimental data. The goal is the identification of RNAs which bind specifically to a regulatory protein. Starting from a case study, done with protein CELF1, we extend our approach and propose three methods …
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