Least Square Support Vector Machines (LS-SVM)
The Application of Least Square Support Vector Machine as a Mathematical Algorithm for Diagnosing Drilling Effectivity in Shaly Formations
... Least Square Support Vector Machines Suykens and Vandewalle proposed a modified version of SVM called least square SVM (LS-SVM) ...
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Quantitative Structure Property Relationships Study of Mobility of Some Benzoaromatic Carboxylate Derivatives by Partial Least Squares and Least-Square Support Vector Machine
... A quantitative structure-property relationship (QSPR) study is suggested for the prediction of mobilities (m) of benzoaromatic carboxylates. Ab initio theory was used to calculate some quantum chemical descriptors ...
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Deformation prediction model of surrounding rock based on GA LSSVM markov
... Command protection engineering is the impor- tant component of national protection engineer- ing system. To raise the level of its construction, a deformation prediction model is given based on Genetic Algorithm (GA), ...
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Hybrid Simulation of a Frame Equipped with MR Damper by Utilizing Least Square Support Vector Machine
... 2. Least square support vector machine (LS-SVM) Supervised learning systems that analyze data and recognize patterns, known as support vector machines (SVMs), are used for ...
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Performance evaluation for engineering project management of particle swarm optimization based on least squares support vector machines
... training. Support vector machine (SVM) is a new machine learning algorithm [4-5], which is theoretically based on structural risk minimization principle ...the least square linear system as ...
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Price predictive analysis mechanism utilizing grey wolf optimizer least squares support vector machines
... uses square errors instead of nonnegative errors in the cost function and applies equality constraint rather inequality constraint of SVM in the problem ...at least quadratic with respect to the number of ...
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Sparseness of Support Vector Machines
... Together with our main theorems Proposition 12 also throws new light on the role of the margin in SVMs: namely, it is not only the margin that gives sparse decision functions but the whole shape of the loss function. ...
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The Corporate Financial Forecasting Based on Least Squares Support Vector Machines Methods
... method Support Vector Machines based on the Statistical learning theory can solve these ...the least square Sup- port Vector Machines to analyze the listed companies’ ...
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River flow time series using least squares support vector machines
... the least squares sup- port vector machine ...mean square error (RMSE) and co- efficient of correlation (R ) are used to evaluate the models’ ...
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Support Vector Machines for Face Recognition
... one in which a nearest neighbor classifier was utilized for classification. Li and Yin [79] presented a framework in which a face picture is initially deteriorated with a wavelet transform to three levels. The ...
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A hybrid model of self organizing maps and least square support vector machine for river flow forecasting
... As a simplification of SVM, Suykens et al. (2002) pro- posed the use of the least squares support vector machines (LSSVM). LSSVM has been used successfully in various areas of pattern ...
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Clustering Via Supervised Support Vector Machines
... Regularized distance kernels introduced in section (3.2.2) (e.g. Gaussian, Laplace and Absdiff) are provably positive-definite [see semigroup book] and therefore satisfy Mercer’s condition. Regularized divergence ...
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Support vector machines in projects risk classification
... different classifications, which can be observed in the shaded areas and delimited by risk lines with minimum average risk limits (represented per ) and maximum low risk (represented by) and maximum average risk and ...
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Support Vector Machine and Least Square Support Vector Machine Stock Forecasting Models
... The objective of this paper is to review the wavelet-based forecasting models through which we would like to test the predictability of the models and compare those without the wavelet-based models. The models are based ...
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Modeling of Corrosion-Fatigue Crack Growth Rate Based on Least Square Support Vector Machine Technique
... radial basis function network and genetic algorithms to optimize the back propagation network for the fatigue crack growth calculations. The results indicate that the applied model can successfully predict the ...
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Study On Prediction Models For Integrated Scheduling In Semiconductor Manufacturing Lines
... Extreme Least Square Support Vector Machine (IELSSVM), which transforms the data into ELM feature space and then minimizes the structural risk like ...
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Support vector machines for texture classification
... these, support vector machines (SVMs) would appear to be a good candidate because of their ability to generalize in high-dimensional spaces, such as spaces spanned by texture ...
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Robustness and Regularization of Support Vector Machines
... We consider regularized support vector machines (SVMs) and show that they are precisely equiva- lent to a new robust optimization formulation. We show that this equivalence of robust optimization and ...
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Covering Numbers and Support Vector Machines
... We have presented a new formula for bounding the covering numbers of SV machines in terms of the eigenvalues of an inte- gral operator induced by the kernel. We showed, by way of an example using a Gaussian ...
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A Novel Approach to Design the Intelligent Technique for Intrusion Detection In Cloud
... The Support Vector Machine approach transforms data into a feature space F that usually has a high dimension. Moreover, SVM generalization depends only on the geometrical characteristics of the training ...
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