Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/FSKD.2013.6816236 | 2013 10TH INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS AND KNOWLEDGE DISCOVERY (FSKD) |
Keywords | DocType | Citations |
Multiple instance learning, Support vector machines, Latent variable models, Stochastic gradient descent. | Conference | 0 |
PageRank | References | Authors |
0.34 | 14 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianjiang Lu | 1 | 259 | 28.23 |
| Wei Li | 2 | 883 | 93.88 |
| Jiabao Wang | 3 | 22 | 11.31 |
| Yafei Zhang | 4 | 15 | 1.73 |
| Yang Li | 5 | 35 | 9.77 |
| Lei Bao | 6 | 5 | 0.74 |