| nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
| 1 |
A graph-theoretic approach to 3D shape classification
|
Ben Hamza, A.
|
|
2016
|
211 |
C |
p. 11-21
11 p.
|
artikel
|
| 2 |
An improved SVM classifier based on double chains quantum genetic algorithm and its application in analogue circuit diagnosis
|
Chen, Peng
|
|
2016
|
211 |
C |
p. 202-211
10 p.
|
artikel
|
| 3 |
A primal–dual method for SVM training
|
Djemai, Samia
|
|
2016
|
211 |
C |
p. 34-40
7 p.
|
artikel
|
| 4 |
A sparse method for least squares twin support vector regression
|
Huang, Huajuan
|
|
2016
|
211 |
C |
p. 150-158
9 p.
|
artikel
|
| 5 |
A vehicle classification system based on hierarchical multi-SVMs in crowded traffic scenes
|
Fu, Huiyuan
|
|
2016
|
211 |
C |
p. 182-190
9 p.
|
artikel
|
| 6 |
Building support vector machines in the context of regularized least squares
|
Peng, Jian-Xun
|
|
2016
|
211 |
C |
p. 129-142
14 p.
|
artikel
|
| 7 |
Computational performance optimization of support vector machine based on support vectors
|
Wang, Xuesong
|
|
2016
|
211 |
C |
p. 66-71
6 p.
|
artikel
|
| 8 |
Curvelet Support Value Filters (CSVFs) for image super-resolution
|
Yang, Shuyuan
|
|
2016
|
211 |
C |
p. 53-59
7 p.
|
artikel
|
| 9 |
Editorial Board
|
|
|
2016
|
211 |
C |
p. IFC-
1 p.
|
artikel
|
| 10 |
Modified twin support vector regression
|
Parastalooi, Nafiseh
|
|
2016
|
211 |
C |
p. 84-97
14 p.
|
artikel
|
| 11 |
Novel Bayesian inference on optimal parameters of support vector machines and its application to industrial survey data classification
|
Zhong, Jingjing
|
|
2016
|
211 |
C |
p. 159-171
13 p.
|
artikel
|
| 12 |
Novel Grouping Method-based support vector machine plus for structured data
|
Hou, Qiuling
|
|
2016
|
211 |
C |
p. 191-201
11 p.
|
artikel
|
| 13 |
Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data
|
Malvoni, M.
|
|
2016
|
211 |
C |
p. 72-83
12 p.
|
artikel
|
| 14 |
Proactive service selection based on acquaintance model and LS-SVM
|
Jingjing, Hu
|
|
2016
|
211 |
C |
p. 60-65
6 p.
|
artikel
|
| 15 |
Probability model selection and parameter evolutionary estimation for clustering imbalanced data without sampling
|
Fan, Jiancong
|
|
2016
|
211 |
C |
p. 172-181
10 p.
|
artikel
|
| 16 |
Real-time online learning of Gaussian mixture model for opacity mapping
|
Zhou, Guo
|
|
2016
|
211 |
C |
p. 212-220
9 p.
|
artikel
|
| 17 |
Recent advances in Support Vector Machines
|
Ding, Shifei
|
|
2016
|
211 |
C |
p. 1-3
3 p.
|
artikel
|
| 18 |
Robust face detection using local CNN and SVM based on kernel combination
|
Tao, Qin-Qin
|
|
2016
|
211 |
C |
p. 98-105
8 p.
|
artikel
|
| 19 |
Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator
|
Yu, Yang
|
|
2016
|
211 |
C |
p. 41-52
12 p.
|
artikel
|
| 20 |
Support vector machine based on hierarchical and dynamical granulation
|
Guo, Husheng
|
|
2016
|
211 |
C |
p. 22-33
12 p.
|
artikel
|
| 21 |
Torque modeling of Switched Reluctance Motor using LSSVM-DE
|
Evangeline S, Jebarani
|
|
2016
|
211 |
C |
p. 117-128
12 p.
|
artikel
|
| 22 |
Toward reliable model for prediction Drilling Fluid Density at wellbore conditions: A LSSVM model
|
Ahmadi, Mohammad Ali
|
|
2016
|
211 |
C |
p. 143-149
7 p.
|
artikel
|
| 23 |
Using unsupervised clustering approach to train the Support Vector Machine for text classification
|
Shafiabady, Niusha
|
|
2016
|
211 |
C |
p. 4-10
7 p.
|
artikel
|
| 24 |
Weighted multicategory nonparallel planes SVM classifiers
|
Kumar, Deepak
|
|
2016
|
211 |
C |
p. 106-116
11 p.
|
artikel
|
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