DocumentCode
2111123
Title
Multiple Instance Support Vector Machines with latent variable description
Author
Jianjiang Lu ; Wei Li ; Jiabao Wang ; Yafei Zhang ; Yang Li ; Lei Bao
Author_Institution
Coll. of Command Inf. Syst., PLA Univ. of Sci. & Technol., Nanjing, China
fYear
2013
fDate
23-25 July 2013
Firstpage
433
Lastpage
438
Abstract
In this paper, the latent variable model is adopted to re-describe MI-SVM and its feature mapping variants. MI-SVM with latent variable description and the corresponding stochastic optimization learning algorithm are proposed. In the Musk and Corel datasets, the proposed algorithm achieves higher predicting accuracy and faster learning speed, with strong stability and robustness for parameters and noise.
Keywords
learning (artificial intelligence); optimisation; stochastic processes; support vector machines; Corel datasets; MI-SVM; Musk datasets; feature mapping variants; latent variable description; multiple instance support vector machines; stochastic optimization learning algorithm; Image recognition; Irrigation; Noise; Radio frequency; Support vector machines; Latent variable models; Multiple instance learning; Stochastic gradient descent; Support vector machines;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Systems and Knowledge Discovery (FSKD), 2013 10th International Conference on
Conference_Location
Shenyang
Type
conf
DOI
10.1109/FSKD.2013.6816236
Filename
6816236
Link To Document