least-squares support-vector machine (LSSVM)
Least Squares Support Vector Machine for Constitutive Modeling of Clay
... Least squares support vector machine (LSSVM) which is a robust type of SVM offers an effective alternative for simulating constitutive modeling of ...of LSSVM models are ...
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A novel fault-tolerant control strategy for near space hypersonic vehicles via least squares support vector machine and backstepping method
... the LSSVM (Least Squares Support Vector Machine)-based inverse system and Backstepping ...the LSSVM in solving the problems with small samples, high dimensions and local ...
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Analysis of Ammonia Nitrogen Content in Water Based on Weighted Least Squares Support Vector Machine (WLSSVM) Algorithm
... [4], support vector machine [5] and other forecasting methods can be used to establish data prediction model, but the neural network requires a large number of training sample data, not suitable for ...
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Segmentation algorithm for Hangzhou white chrysanthemums based on least squares support vector machine
... Abstract: In order to realize the visual positioning for Hangzhou white chrysanthemums harvesting robot in natural environment, a color image segmentation method for Hangzhou white chrysanthemum based on least ...
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Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
... Abstract. Image processing of X-ray-computed polychro- matic cone-beam micro-tomography (µXCT) data of geo- logical samples mainly involves artefact reduction and phase segmentation. For the former, the main ...
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Classification of Clustered Microcalcifications in Mammograms using Particle Swarm Optimization and Least Squares Support Vector Machine
... PSO- LSSVM approach has adopted an objective function [12] that optimized the generalization ability (classification accuracy ) of the proposed CADx by selecting the best and smallest subset of shape features and ...
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Applying ANN, ANFIS, and LSSVM Models for Estimation of Acid Solvent Solubility in Supercritical CO
... network, Least squares support vector machine and adaptive neuro-fuzzy inference system are developed to predict the solubility of different acids in carbon dioxide based on the ...
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Hybridizing GMDH and least squares SVM support vector machine for forecasting tourism demand
... handling least squares support vector machine (GLSSVM) algorithm, which combines the theory a group method of data handling (GMDH) with the least squares support ...
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Machine learning prediction Models of Electrical Efficiency of Photovoltaic-Thermal Collectors
... the least pollution impact between the available alternatives of fossil ...investigation, machine leaening approaches of neural networks (NN), neuro-fuzzy and least squares support ...
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← Return to Article Details ENHANCEMENT SPECTRAL RESOLUTION FOR THE PREDICTION AMOUNT OF SOFOSBUVIR AND LEDIPASVIR USING LEAST SQUARES SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORKS IN PHARMACEUTICAL FORMULATION
... The network was formed with 2 and 5 layers and 2, 5, 7, 9 neurons in each layer for SOF and LED. Hyperbolic tangent sigmoid (TANSIG) and a linear (PURELIN) as transfer functions were selected for the first and second ...
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Time Series Forecasting with Missing Values
... Abstract—Time series prediction has become more popular in various kinds of applications such as weather prediction, control engineering, financial analysis, industrial monitoring, etc. To deal with real-world problems, ...
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Simulated annealing least squares twin support vector machine (SA LSTSVM) for pattern classification
... of LSSVM (Suykens and Vandewalle 1999; Mitra et ...“least squares of errors” to mod- ify inequality constraints in TSVM to equality constraints by solving a set of linear equations rather than two ...
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Simulated annealing least squares twin support vector machine (SA LSTSVM) for pattern classification
... of LSSVM [8, 20] and TSVM. LSTSVM employs “least squares of errors” to modify inequal- ity constraints in TSVM to equality constraints by solving a set of linear equa- tions rather than two Quadratic ...
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Using Least Squares Support Vector Machines for Frequency Estimation
... Frequency estimation is transformed to a pattern recognition problem, and a least squares support vector machine (LS-SVM) estimator is derived. The estimator can work efficiently ...
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Algorithm Model and Performance Evaluation of P2P Network System under the Internet of Things
... First, investigation of a mapping model of packet loss rate and the Quality of experience [1] . Second, Research on Packet loss rate mapped to the Quality of experience [2] . Third, Investigation of the influencing ...
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The Improved Least Square Support Vector Machine Based on Wolf Pack Algorithm and Data Inconsistency Rate for Cost Prediction of Substation Projects
... improved least squares support vector machine (ILSSVM) optimized by wolf pack algorithm(WPA) is proposed to improve the accuracy and stability of the cost forecasting of substation ...
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Application of LSSVM to logistics demand forecasting based on grey relational analysis and kernel principal component analysis
... nonlinear principal components as inputs. The forecasting performance of the GRA-KPCA-LSSVM model is investigated through the use of China logistics data. The results show that the GRA-KPCA-LSSVM model has ...
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New Intelligent Classification Techniques for Diagnosis of Diabetes Mellitus based on Modified PSO
... classifier, Support Vector Machine, Least Squares Support Vector Machine, Particle Swarm ...of machine learning ...
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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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Optimising Kernel Parameters and Regularisation Coefficients for Non-linear Discriminant Analysis
... Another related approach is known as the proximal support vector machine or P-SVM, proposed by Fung and Mangasarian (2001). A P-SVM is very close to LS-SVM in the sense that both of them consider ...
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