scholarly journals Rock Burst Monitoring and Early Warning Based on Incremental Learning Method with SVM

2013 ◽  
Vol 5 (4) ◽  
pp. 121-124 ◽  
Author(s):  
Chunfang Wu ◽  
Ruisheng Jia ◽  
Tao Qiu
2011 ◽  
Vol 74 (11) ◽  
pp. 1800-1808 ◽  
Author(s):  
David Martínez-Rego ◽  
Beatriz Pérez-Sánchez ◽  
Oscar Fontenla-Romero ◽  
Amparo Alonso-Betanzos

Author(s):  
Tadahiro Oyama ◽  
Stephen Karungaru ◽  
Satoru Tsuge ◽  
Yasue Mitsukura ◽  
Minoru Fukumi

2011 ◽  
Vol 216 ◽  
pp. 301-306
Author(s):  
Shi Hua Zhang ◽  
Xi Long Qu ◽  
Xue Ye Wang

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).


2014 ◽  
Vol 602-605 ◽  
pp. 3843-3845
Author(s):  
Xue Bing She

Now that the endless spam messages have sevely affected people on their works and daliy lives. The omission and even the help of the serrvice providrs (SP) in this regard make it extremely necessary to distinguish and sort the spam messages through filtering the messages on user’s mobile phone. The paper expatiated on a “self-study spam messages distinguishing and soring system’ developed onARM9 platform. By adding a spam box to SMS software of cellphome besidrs all os its normal functions, the system incessantly adjusts the weight of the featues though incremental learning method so as to achieve the highly accurate discrimination on whether a message received is the spam one or not, and futher decides to sort the message to the message box or the spam box, Test result on ARM9 platform shows, our technology can be applied completely to the mobile phones with just general performance.


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