Recognition of emotion with SVMs

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4114 LNAI - II, Issue , 2006, Pages 701-710

  Teng, Zhi ^a   Ren, Fuji ^a,b   Kuroiwa, Shingo ^a  

^a UNIVERSITY OF TOKUSHIMA   (Japan)

^b BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTER KEYBOARDS; LEARNING SYSTEMS; MACHINERY; PERSONNEL TRAINING;

EMOTION RECOGNITION SYSTEM; NOVELTY DETECTION; SUPPORT VECTOR MACHINES (SVMS); TRAINING SET;

HUMAN REACTION TIME;

EID: 33749542026     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11816171_87     Document Type: Conference Paper

References (12)

1. Cowie, R., Douglas ,C.E., Tsapatsoulis , N., Votsis, G., Kollias, S., Fellenz, W., Taylor, W.G. : Emotion Recognition in Human-computer Interaction, IEEE Sig. Proc. Mag. 18 (2001) 32-80
2. Lee, C.M., Narayanan, S., Pieraccini, R.: Recognition of Negative Emotions from the Speech Signal, In Proc. Automatic Speech Recognition and Understanding (2001)
3. Ren, F.J.: Recognizing Human Emotion and Creating Machine Emotion, Information. 8 (2005) 7-20
4. Jiang, P.L., Xiang, H., Ren, F.J., Kuroiwa, S.: An Advanced Mental State Transtion Network and Psychological Experiments, Lecture Notes in Computer Science, Vol. 3824 Springer-Verlag GmbH (2005) 1026-1035
5. http://www.clopinet.com/isabelle/Projects/SVM/applist.html
6. Vapnik, V.: Statistical Learning Thoery. Wiley (1998)
7. Plutchik, R.: The Psychology and Biology of Emotion, HarperCollins College, New York. (1994)
8. Chang, C.C., Lin, C.J.: Libsvm: A Library for Support Vector Machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. 2001
9. Cristianini, N., Shaw, E.J.: An Introduction to Support Vector Machines and other Kernel-based Learning Methods, Cambridge University Press. (2000)
10. Haussler ,D. : Convolution Kernels on Discrete Structures, UC Santa Cruz Technical Report UCS-CRL-99-10(1999)
11. Shaw, E.J.: Condencestimates of Classification Accuracy on New Examples. In S. Ben-David (ed.), EuroCOLT97, Lecture Notes in Artificial Intelligence. 1208 (1997) 260-271
12. Campbell, C.: ESANN2000 Proceedings- European Symposium on Artificial Neural Networks Bruges (Belgium).26-28 April 2000.DFacto. ISBN 2-930307-00-5 27-36