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RRegrs: an R package for computer-aided model selection with multiple regression modelsQSAR-based models for designing quinazoline/imidazothiazoles/pyrazolopyrimidines based inhibitors against wild and mutant EGFRPredicting Fatigue and Psychophysiological Test Performance from Speech for Safety-Critical Environments.Resolution-by-proxy: a simple measure for assessing and comparing the overall quality of NMR protein structures.Temporal analysis of the usage log of a research networking systemImproved Correction of Atmospheric Pressure Data Obtained by Smartphones through Machine LearningModerate predictive value of demographic and behavioral characteristics for a diagnosis of HPV16-positive and HPV16-negative head and neck cancer.In-silico prediction of disorder content using hybrid sequence representation.Classification and clustering analysis of pyruvate dehydrogenase enzyme based on their physicochemical properties.Predicting MHC-II binding affinity using multiple instance regression.Forecasting Occurrences of Activities.How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.Optimizing the macrocyclic diterpenic core toward the reversal of multidrug resistance in cancer.AVCpred: an integrated web server for prediction and design of antiviral compounds.Quantitative Regression Models for the Prediction of Chemical Properties by an Efficient Workflow.Vermont: a multi-perspective visual interactive platform for mutational analysis.DUET: a server for predicting effects of mutations on protein stability using an integrated computational approach.Modeling plan-related clinical complications using machine learning tools in a multiplan IMRT framework.Optimal experimental conditions for Welan gum production by support vector regression and adaptive genetic algorithmTeaching human poses interactively to a social robot.Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis.Developing a fuzzy neural network-based support vector regression (FNN-SVR) for regionalizing nitrate concentration in groundwater.Predicting DNA-binding sites of proteins based on sequential and 3D structural information.Training nu-support vector regression: theory and algorithms.MetStabOn-Online Platform for Metabolic Stability Predictions.Adaptive bacteria colony picking in unstructured environments using intensity histogram and unascertained LS-SVM classifier.Modelling of process parameters in laser polishing of steel components using ensembles of regression treesAllometric Equations for Estimating Biomass and Carbon Stocks in the Temperate Forests of North-Western MexicoMachine Learning Applied to Optometry DataA New Adaptive LSSVR with Online Multikernel RBF Tuning to Evaluate Analog Circuit PerformanceThe Effectiveness of Feature Selection Method in Solar Power PredictionResearch on Amplifier Performance Evaluation Based onδ-Support Vector RegressionImproving the Operational Methodology of Tropical Cyclone Seasonal Prediction in the Australian and the South Pacific Ocean RegionsNew Strategy for Analog Circuit Performance Evaluation under Disturbance and Fault Value
P2860
Q27134949-A55412E9-7C19-4DB9-9CE2-4BFB9C6A52D1Q28540324-54B62908-6583-4305-8BB6-57EC03EF7D54Q30402947-F4654510-A4DE-48DA-B87B-43DC74C56A8EQ30417724-170B5CDA-EDA8-41CC-BB04-E39A492A3CA3Q30487933-D9C0D9DD-9319-4244-B6CF-6ABD912D3A08Q31121714-C0E1B4B0-6C04-4CC3-B09D-4645EC3F61E3Q33639789-654C38A5-58EA-4FA4-8C63-803FDDCDC217Q33935454-99098DC8-64EC-492F-B20F-9999F473EE32Q34183987-A7A08573-E959-45C3-9258-ED71424B4430Q36105054-94B80557-0E74-46DB-9D1E-03982AD38529Q38659745-A28CC32A-A7D6-46D9-9744-52A39BA3B1C6Q38756001-ECBFBFBC-BF2B-46E2-95BF-0EDE9A55F830Q38877337-52AB7E97-05A4-429A-9332-E1A695C1AA1BQ39524689-095D60F4-F0EB-40BE-BBF0-2D4B5CB48531Q39524736-22A4AA8F-8904-4D59-B303-3636DCFF7CF6Q41695683-58FAD48A-703E-402C-AEB0-C5ADADB1DA0AQ41886851-79CEEFEB-BD68-410E-9F1F-7B909F723603Q41943970-592C426B-120C-4A6F-B6DC-FCC77058318EQ42696339-25712189-A144-45AD-B208-C668F2A3FA55Q42917183-61DAEA51-2600-4AB6-8D31-7983DA7E8A94Q45946082-2C5893F2-28E6-4383-B316-3AB4B66501FCQ48086809-A68E8648-8A0E-436C-8101-BED4510888E0Q51121458-C35671BB-FA5B-49D7-B4A8-A4ADE469E44CQ52034609-9D4D5613-7123-4F38-BFB0-E5C6C65D3C2EQ52337060-C50897B2-F737-459C-92DE-81942A5898D5Q53519428-50D8BD65-4718-479F-BA62-DF8268EF5F62Q58196055-315935B4-2AD7-4FCC-9786-F61FFB5708D4Q58307945-DBE4D643-7759-455C-B4A3-C3B2B1A38EF1Q58887349-E517A3BC-8AF7-4CC0-8386-3B259DA80D50Q58915605-D4548972-06DB-4962-81CB-6760B8776CF8Q59021821-4EAA6E7F-71B7-470B-8755-B5BDADDAB143Q59039573-D7EA0901-98D7-46B0-8EED-EC98776CADBEQ59045705-B8B53327-B475-43E1-9348-3A3257034CA7Q59071717-99C1AFA4-2C18-41B3-A53D-FCC95FAD6119
P2860
description
2000 nî lūn-bûn
@nan
2000年の論文
@ja
2000年学术文章
@wuu
2000年学术文章
@zh
2000年学术文章
@zh-cn
2000年学术文章
@zh-hans
2000年学术文章
@zh-my
2000年学术文章
@zh-sg
2000年學術文章
@yue
2000年學術文章
@zh-hant
name
Improvements to the SMO algorithm for SVM regression.
@en
Improvements to the SMO algorithm for SVM regression.
@nl
type
label
Improvements to the SMO algorithm for SVM regression.
@en
Improvements to the SMO algorithm for SVM regression.
@nl
prefLabel
Improvements to the SMO algorithm for SVM regression.
@en
Improvements to the SMO algorithm for SVM regression.
@nl
P2093
P356
P1476
Improvements to the SMO algorithm for SVM regression.
@en
P2093
C Bhattacharyya
K K Murthy
S K Shevade
S S Keerthi
P304
P356
10.1109/72.870050
P577
2000-01-01T00:00:00Z