Support Vector Machines
Bagging Support Vector Machines for Leukemia Classification
... Leukemia is one of the most common cancer type, and its diagnosis and classification is becoming increasingly complex and important. Here, we used a gene expression dataset and adapted bagging support ...
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A comparison of four data selection methods for artificial neural networks and support vector machines
... and Support Vector Machines (SVMs) will take place, the main objective of this paper is to analyse the performance of four data selection methods, including Random Data Selection (RDS), Convex Hull ...
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Classificação automática de páginas Web Multi-label via MDL e Support Vector Machines
... por Support Vector Machines de até 7 pontos percentuais, simplesmente por marcar diferentemente as palavras provenientes dos títulos das páginas ...
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Electricity load demand forecasting in Portugal using least-squares support vector machines
... Least-Squares Support Vector Machines (LS-SVMs) are a good alternative to RBF ANN and other approaches, since they have fewer parameters to adjust, hence, allowing sig- nificant decrease in the ...
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A hybrid least squares support vector machines and GMDH approach for river flow forecasting
... Least squares support vector machines (LSSVM), as a modification of SVM which was introduced by Suykens (1999). The method uses equality constraints instead of inequality constraints and adopts the ...
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River flow time series using least squares support vector machines
... Least squares support vector machines (LSSVM), as a modification of SVM was introduced by Suykens and Van- dewalle (1999). LSSVM is a simplified form of SVM that uses equality constraints instead of ...
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Reconstructed State Space Model for Recognition of Consonant - Vowel (Cv) Utterances Using Support Vector Machines
... There have been a lot of well known attempts reported in the literature towards automatic speech recognition of CV speech units which kept the research in this area effective and vibrant. Some of them are Mel Frequency ...
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A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems
... Kernel methods and support vector machines have become the most popular learning from examples paradigms. Several areas of application research make use of SVM approaches as for instance hand written ...
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Detection of Oil Production Abnormalities in a Cluster of Wells System Using Fuzzy Support Vector Machines
... This paper presents a novel framework to detect low oil production i.e., well with abnormal production patterns indicating possible formation damage. An hybrid combination of fuzzy rule based inference system and ...
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Comparing support vector machines and artificial neural networks in the recognition of steering angle for driving of mobile robots through paths in plantations
... a support vector machine and an artificial neural network, which receives the pixels of the translated skeleton and determines an output that represents the steering angle to the pattern of pixels ...of ...
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Determination of chemical constituents in eucalyptus wood by Py-GC/MS and multivariate calibration: comparison between artificial neural network and support vector machines.
... AND SUPPORT VECTOR ...- Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by ...
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Determinação de vícios refrativos oculares utilizando Support Vector Machines.
... astigmatismo) por meio de uma técnica de Aprendizado de Máquina. Esses vícios são diagnosticados a partir da análise de imagens do olho adquiridas por uma técnica específica, denominada Hartmann-Shack (ou ...
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Biased Support Vector Machines and Kernel Methods for Intrusion Detection
... In our experiments, we perform 5-class classification using different kernel methods [17]. The (training and testing) data set contains 11982 randomly generated points from the data set representing the five classes, ...
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Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines
... In its origin, the SVM was developed to solve binary classification problems with linearly separable classes. However, the same principles can be applied to solve one-class problems—also known as novelty detection ...
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Support vector machines and kernels for computational biology.
... a vector space representation is not necessarily available? There is a straightforward way of turning a linear classifier nonlinear, or making it applicable to nonvectorial ...some vector space, which we ...
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Support Vector Machines na previsão do comportamento de uma ETAR
... (1996a) refere que o DM consiste numa das fases do processo KDD. A fase inicial consiste na seleção de dados, que inclui a aprendizagem do domínio da aplicação e a escolha do conjunto d[r] ...
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Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
... We would like to thank the symposium presenters and participants who made this special issue possible. Special thanks go to the editors of BMC Bioinformatics who advised us in preparing the manuscripts. Finally we ...
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Packet Classification using Support Vector Machines with String Kernels
... A kernel is a function that enables the support vector machine to linearly separate the data in a higher-dimensional space . Using a kernel function is similar to adding a trivial feature to the already ...
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Improving Text Categorization By Using A Topic Model
... and Support Vector Machines (SVM) ...using Support Vector Machines (Topic_Model_SVM) to learn the classification model, however, yielded a higher performance compared to using ...
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INTEGRATION OF IMAGE-DERIVED AND POS-DERIVED FEATURES FOR IMAGE BLUR DETECTION
... Platt, J., 1999. Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: support vector learning (Bernhard, S., Christopher, J.C.B., ...
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