Location estimation in indoor wireless networks by hierarchical support vector machines with fast learning algorithm

2010 ◽  
Author(s):  
Vin Sen Feng ◽  
Tai Chi Wang ◽  
Shih Yu Chang ◽  
Hsi-Pin Ma
Keyword(s):  
Wireless Networks ◽  
Support Vector Machines ◽  
Learning Algorithm ◽  
Location Estimation ◽  
Support Vector ◽  
Fast Learning ◽  
Indoor Wireless ◽  
Vector Machines

scholarly journals A fast learning algorithm for one-class slab support vector machines

2021 ◽  
pp. 107267
Author(s):  
Bagesh Kumar ◽  
Ayush Sinha ◽  
Sourin Chakrabarti ◽  
O.P. Vyas
Keyword(s):  
Support Vector Machines ◽  
Learning Algorithm ◽  
Support Vector ◽  
Fast Learning ◽  
Vector Machines

New Incremental Learning Algorithm With Support Vector Machines

2019 ◽  
Vol 49 (11) ◽  
pp. 2230-2241 ◽  
Author(s):  
Jie Xu ◽  
Chen Xu ◽  
Bin Zou ◽  
Yuan Yan Tang ◽  
Jiangtao Peng ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Support Vector ◽  
Vector Machines

Improved Learning Algorithm Based on Semi-Supervised Support Vector

2011 ◽  
Vol 474-476 ◽  
pp. 1-6
Author(s):  
Guo Xing Peng ◽  
Bei Li
Keyword(s):  
Support Vector Machines ◽  
Lower Bound ◽  
Simulation Analysis ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
Support Vector ◽  
Dual Function ◽  
Data Set ◽  
Vector Machines ◽  
Improved Learning

Improved learning algorithm for branch and bound for semi-supervised support vector machines is proposed, according to the greater difference in the optimal solution in different semi-supervised support vector machines for the same data set caused by the local optimization. The lower bound of node in IBBS3VM algorithm is re-defined, which will be pseudo-dual function value as the lower bound of node to avoid the large amount of calculation of 0-1 quadratic programming, reducing the lower bound of each node calculate the time complexity; at the same time, in determining the branch nodes, only based on the credibility of the unlabeled samples without the need to repeatedly carry out the training of support vector machines to enhance the training speed of the algorithm. Simulation analysis shows that IBBS3VM presented in this paper has faster training speed than BBS3VM algorithms, higher precision and stronger robustness than the other semi-supervised support vector machines.

An incremental learning algorithm for Lagrangian support vector machines

2009 ◽  
Vol 30 (15) ◽  
pp. 1384-1391 ◽  
Author(s):  
Hua Duan ◽  
Xiaojian Shao ◽  
Weizhen Hou ◽  
Guoping He ◽  
Qingtian Zeng
Keyword(s):  
Support Vector Machines ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Support Vector ◽  
Vector Machines ◽  
Lagrangian Support Vector Machines
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