Support Vector
aula 13 - Support Vector Machines
... Support Vector Machines (SVM) é um algoritmo de aprendizagem supervisionado para classificação de dados, semelhante à regressão logística e às redes ...
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A Computer Aided Diagnosis System for Lung Cancer Detection Using Support Vector Machine
... contrast and shapes. Simple rule based classifications on such features tend to produce a lot of false positives. To overcome these problems, the author proposed a Computer Aided Diagnosing (CAD) (Ginneken et al., 2001) ...
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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 sensitivity ...
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A multi-label learning based kernel automatic recommendation method for support vector machine.
... Meta-features. The meta-features consist of measures extracted from data sets for uni- formly depicting data set characteristics. Pavel et al. [54] first proposed to generate a set of rules for characterizing the ...
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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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Classificação automática de páginas Web Multi-label via MDL e Support Vector Machines
... Tradicionalmente, o princípio MDL é visto como composto de duas partes independentes: modelos e dados. Assim sendo, para determinados dados, o modelo cuja soma de seu tamanho com o tamanho dos dados comprimidos seja a ...
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AADT prediction using support vector regression with data-dependent parameters
... A modified support vector regression (SVR) approach has been proposed for future-year AADT estimation. The modified SVR uses data-dependent parameters in order to reduce computational time and to achieve ...
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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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Forecasting bus passenger flows by using a clustering-based support vector regression approach
... propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear ...A support vector regression (SVR) is then exploited to forecast the passenger flow for ...
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Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine
... 2002). Support vector machine maps the input vectors into a high dimensional feature space through nonlinear ...searched. Support Vector Machine (SVM) is very effective method for general ...
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Support vector machine ensemble based on feature and hyperparameter variation
... We present a generic procedure for diagnosing faults using features extracted from noninvasive machine signals, based on supervised learning techniques to build the fault classifiers. An important novelty of our research ...
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Support vector machines and particle swarm optimization applied to reliability prediction
... Reliability is a critical metric for organizations since it directly influences their performance in face of the market competition, as well as is essential in maintaining their production systems available. The ...
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Comparison of support vector machine and object based classification methods for coastline detection
... Maiti, S. and Bhattacharya, A.K., 2009. Shoreline change analysis and its application to prediction: A remote sensing and statistics based approach, Marine Geology, 257, 11–23. Song, X., Duan, Z., Jiang, X., 2012. ...
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Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites
... Received: 5 June 2020; Accepted: 17 July 2020; Published: 19 July 2020 Abstract: Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area ...
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Rating de risco de projetos de inovação tecnológica: uma proposta através da aplicação das Support Vector Machines
... A utilização de um sistema de classificação de rating para projetos de inovação pode ser ampliada e avaliada quando são aplicados algoritmos de inteligência artificial, como por exemplo, as Support Vector ...
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EMG Diagnosis via AR Modeling and Binary Support Vector Machine Classification
... There are more than 100 neuromuscular disorders that affect the brain, spinal cord, nerves and muscles. Many of these diseases are hereditary and life expectancy of many sufferers is considerably reduced. Early detection ...
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Specific Land Cover Class Mapping by Semi-Supervised Weighted Support Vector Machines
... Literature shows that there are essentially two alternatives to the standard multi-class supervised approach: the binarisation strategy and one-class learning algorithms [21–23]. With binarisation strategy, users ...
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A Novel Classification Algorithm Based on Incremental Semi-Supervised Support Vector Machine.
... For current computational intelligence techniques, a major challenge is how to learn new concepts in changing environment. Traditional learning schemes could not adequately address this problem due to a lack of dynamic ...
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lncRScan-SVM: A Tool for Predicting Long Non-Coding RNAs Using Support Vector Machine.
... Functional long non-coding RNAs (lncRNAs) have been bringing novel insight into biologi- cal study, however it is still not trivial to accurately distinguish the lncRNA transcripts (LNCTs) from the protein coding ones ...
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Weighted Twin Support Vector Machine with Universum
... Universum, which is defined as the sample that does not belong to either class of the classification problem of interest, has been proved to be helpful in supervised learning. In this paper, we have proposed a new ...
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