Support vector regression
Forecasting the Prices of TAIEX Options by Using Genetic Programming and Support Vector Regression
... and support vector regression (SVR) to forecast the prices of stock options based on the predictors consisting of the six basic factors in the B-S model and the other factors, including the opening ...
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Maximal Information Coefficient and Support Vector Regression Based Nonlinear Feature Selection and QSAR Modeling on Toxicity of Alcohol Compounds to Tadpoles of Rana temporaria
... ability. Support vector machine (SVM) is a strong performer in the machine learning field, which is built on the statistical learning theory and the minimum structural ...6-8 Support vector ...
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Prodepth: predict residue depth by support vector regression approach from protein sequences only.
... Support vector machine is a sophisticated supervised machine learning technique that is built based on statistical learning theory [59,60] and has been widely used in the applications of ...that ...
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Forecasting bus passenger flows by using a clustering-based support vector regression approach
... Based on the preceding analyses, to adjust the fine charac- teristics of bus passenger flows, this paper introduces a novel forecasting model based on clustering and nonlinear simula- tion: an affinity propagation-based ...
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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 ...
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THE SUPPORT VECTOR REGRESSION WITH THE PARAMETER TUNING ASSISTED BY A DIFFERENTIAL EVOLUTION TECHNIQUE: STUDY OF THE CRITICAL VELOCITY OF A SLURRY FLOW IN A PIPELINE
... target vector and the noisy random vector to produce the trial vector ...trial vector and the target vector is performed and the winner is replaced into the ...
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Lat. Am. j. solids struct. vol.12 número2
... and support vector machine (Wang and Shan, 2007 and Vapnik, ...defined support vector machine as a learning machine strategy based on a learning algorithm and on a specific kernel that ...
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Predicting Market Impact Costs Using Nonparametric Machine Learning Models.
... and support vector regression, to predict mar- ket impact cost accurately and to provide the predictive model that is versatile in the number of ...
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An Efficient and Simplified Model for Forecasting using SRM
... (Support Vector Machines), provides robust and accurate results, however it may require intense computation and other ...(Support Vector Regression) for forecasting the retail sales of ...
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Kriging Analysis In The Spatial Domain For Dispersion Models
... γ h is a two parameter family of curves . It may be spherical, exponential, power, Gaussian, cubic, etc. ii) A new paradigm [3] analyzing and learning from data is called support vector machines (SVM). Now ...
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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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Body size and allometric shape variation in the molly Poecilia vivipara along a gradient of salinity and predation
... Multivariate regression analysis showed that size variation among populations accounted for 66% of shape variation in females and 38% in males, suggesting that size is the most important dimension underlying shape ...
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Classification of Brain Tumor Using Support Vector Machine Classfiers
... Kernel function is used when decision function is not a linear function of the data and the data will be mapped from the input space through a non linear transformation rather than fitting non- linear curves to the ...
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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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Utilização de Técnicas de Aprendizado de Máquina para Acompanhamento de Fóruns Educacionais
... With the growth of distance education, and the adoption of computational systems as an educational support tool, the use of virtual learning environments has also grown. These environments offer various tools that ...
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Programa computacional para a identificação automática de exoplanetas
... do support vector machine, entretanto a diferença entre ambas é pequena, menos de dez por cento, mostrando assim como a aplicação da técnica de gráficos de recorrência de fato acrescentou positivamente no ...
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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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A Survey on Potential of the Support Vector Machines in Solving Classification and Regression Problems
... and support vector machines have become the most popular learning from examples ...and regression tasks as well as to analyze the computational complexity corresponding to different methodologies ...
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A Computer Aided Diagnosis System for Lung Cancer Detection Using Support Vector Machine
... The experiments are conducted on the proposed computer-aided diagnosis systems with the help of lung images obtained from the reputed hospital. This experimentation data consists of 1000 lung images. Those 1000 lung ...
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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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