A Novel Feature Incremental Learning Method for Sensor-Based Activity Recognition

2019 ◽  
Vol 31 (6) ◽  
pp. 1038-1050 ◽  
Author(s):  
Chunyu Hu ◽  
Yiqiang Chen ◽  
Xiaohui Peng ◽  
Han Yu ◽  
Chenlong Gao ◽  
...  
Keyword(s):  
Activity Recognition ◽  
Incremental Learning ◽  
Learning Method ◽  
Incremental Learning Method

A robust incremental learning method for non-stationary environments

2011 ◽  
Vol 74 (11) ◽  
pp. 1800-1808 ◽  
Author(s):  
David Martínez-Rego ◽  
Beatriz Pérez-Sánchez ◽  
Oscar Fontenla-Romero ◽  
Amparo Alonso-Betanzos
Keyword(s):  
Incremental Learning ◽  
Learning Method ◽  
Incremental Learning Method

Incremental Learning Method of Simple-PCA

2008 ◽  
pp. 403-410 ◽  
Author(s):  
Tadahiro Oyama ◽  
Stephen Karungaru ◽  
Satoru Tsuge ◽  
Yasue Mitsukura ◽  
Minoru Fukumi
Keyword(s):  
Incremental Learning ◽  
Learning Method ◽  
Incremental Learning Method

Incremental Regressive Learning Algorithm of Support Vector Machine and its Application

2011 ◽  
Vol 216 ◽  
pp. 301-306
Author(s):  
Shi Hua Zhang ◽  
Xi Long Qu ◽  
Xue Ye Wang
Keyword(s):  
Support Vector Machine ◽  
Transition Temperature ◽  
Incremental Learning ◽  
Large Scale ◽  
Learning Algorithm ◽  
Learning Ability ◽  
Support Vector ◽  
Learning Method ◽  
Essential Characteristic ◽  
Incremental Learning Method

There is no incremental learning ability for the traditional support vector machine (SVM) and there are all kind of merits and flaws for usually used incremental learning method. Normal SVM is unable to train in large-scale samples, while the computer’s memory is limited. In order to resolve this problem and improve training speed of the SVM, we analyze essential characteristic of SVM and bring up the incremental learning algorithm of SVM based on regression of SVM related to SV (support vectors). The algorithm increases the speed of training and can be able to learning with large-scale samples while its regressive precision loses fewer. The experiments show that SVM performs effectively and practically. Its application to prediction of the transition temperature (Tg) for high molecular polymers show that this model (R2=0.9427) proved to be considerably more accurate compared to a ANNs regression model (R2=0.9269).

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