Emotion detection via discriminative kernel method

Author(s):  
Hua Wang ◽  
Heng Huang ◽  
Yanzi Hu ◽  
Mindi Anderson ◽  
Pamela Rollins ◽  
...  

2014 ◽  
Vol 7 (1) ◽  
pp. 107
Author(s):  
Ilyes Elaissi ◽  
Okba Taouali ◽  
Messaoud Hassani


Author(s):  
Hirosato SEKI ◽  
Fuhito MIZUGUCHI ◽  
Satoshi WATANABE ◽  
Hiroaki ISHII ◽  
Masaharu MIZUMOTO




2013 ◽  
Vol 756-759 ◽  
pp. 3652-3658
Author(s):  
You Li Lu ◽  
Jun Luo

Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.







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