Search Result "least squares supporting vector machine"
QSAR Study On 5-Lipoxygenase Inhibitors Based on Support Vector Machine
Journal: Medicinal Chemistry
Volume: 8 Issue: 6 Year: 2012 Page: 1108-1116
Author(s): Bing Niu, Qiang Su, Xiaochen Yuan, Wencong Lu, Juan Ding
Homology-Free Prediction of Functional Class of Proteins and Peptides by Support Vector Machines
Journal: Current Protein & Peptide Science
Volume: 9 Issue: 1 Year: 2008 Page: 70-95
Author(s): Y. Z. Chen, F. Zhu, L. Y. Han, X. Chen, H. H. Lin, S. Ong, B. Xie, H. L. Zhang
Kinematic Calibration of Parallel Robots Based on Least Squares Algorithm
Journal: Recent Patents on Mechanical Engineering
Volume: 4 Issue: 3 Year: 2011 Page: 226-233
Author(s): Dayong Yu, Qiang Zhang
Prediction of Protein Structure Classes with Pseudo Amino Acid Composition and Fuzzy Support Vector Machine Network
Journal: Protein & Peptide Letters
Volume: 14 Issue: 8 Year: 2007 Page: 811-815
Author(s): Yong-Sheng Ding, Tong-Liang Zhang, Kuo-Chen Chou
Prediction of Protein-Protein Interactions Based on Protein-Protein Correlation Using Least Squares Regression
Journal: Current Protein & Peptide Science
Volume: 15 Issue: 6 Year: 2014 Page: 553-560
Author(s): De-Shuang Huang,Lei Zhang,Kyungsook Han,Suping Deng,Kai Yang,Hongbo Zhang
Prediction of Cytochrome P450 2B6-Substrate Interactions Using Pharmacophore Ensemble/Support Vector Machine (PhE/SVM) Approach
Journal: Medicinal Chemistry
Volume: 4 Issue: 4 Year: 2008 Page: 396-406
Author(s): Max K. Leong, Tzu-Hsien Chen
Machine Learning Quantitative Structure-Activity Relationships (QSAR) for Peptides Binding to the Human Amphiphysin-1 SH3 Domain
Journal: Current Proteomics
Volume: 6 Issue: 4 Year: 2009 Page: 289-302
Author(s): Ovidiu Ivanciuc
Boosting Granular Support Vector Machines for the Accurate Prediction of Protein-Nucleotide Binding Sites
Journal: Combinatorial Chemistry & High Throughput Screening
Volume: 22 Issue: 7 Year: 2019 Page: 455-469
Author(s): Yi-Heng Zhu,Jun Hu,Yong Qi,Xiao-Ning Song,Dong-Jun Yu
Using Machine Learning Methods to Predict Experimental High Throughput Screening Data
Journal: Combinatorial Chemistry & High Throughput Screening
Volume: 13 Issue: 5 Year: 2010 Page: 430-441
Author(s): Cherif Mballo, Vladimir Makarenkov
Rapid Determining Contents of the Rhubarb Anthraquinones Compounds by Support Vector Machine Modeling based on Near Infrared Spectra
Journal: Current Analytical Chemistry
Volume: 17 Issue: 3 Year: 2021 Page: 396-407
Author(s): Linqi Liu,JInhua Luo,Chenxi Zhao,Bingxue Zhang,Wei Fan,Fuyou Du