| Presentation | 2018-09-20 Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning Shota Utsumi, Keisuke Kameyama, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The accuracy of each weak learner and acquisition of complementary functions among weak learners are important for improving the generalization performance in Ensemble Learning. In this paper, we propose a method in which weak learners are trained in parallel based on the weights of the samples used in Boosting. A sample which has large weight is misclassified by many weak learners, thus we aim to improve complementation between weak learners by considering such heavily weighted samples in training weak learners. Through experiments, it was found that the complementation between weak learners and the accuracies of the combined learner of the proposed method are superior to the conventional method. Additionally we discuss complementation in weak learners and accuracies of the proposed method. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | Ensemble Learning / Boosting / AdaBoost / Error Correcting Output Codes / Multi-Class Classification |
| Paper # | PRMU2018-37,IBISML2018-14 |
| Date of Issue | 2018-09-13 (PRMU, IBISML) |
| Conference Information | |
| Committee | PRMU / IBISML / IPSJ-CVIM |
|---|---|
| Conference Date | 2018/9/20(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Chair | Shinichi Sato(NII) / Hisashi Kashima(Kyoto Univ.) |
| Vice Chair | Yoshihisa Ijiri(Omron) / Toru Tamaki(Hiroshima Univ.) / Masashi Sugiyama(Univ. of Tokyo) / Koji Tsuda(Univ. of Tokyo) |
| Secretary | Yoshihisa Ijiri(NEC) / Toru Tamaki(Osaka Univ.) / Masashi Sugiyama(Nagoya Inst. of Tech.) / Koji Tsuda(AIST) |
| Assistant | Go Irie(NTT) / Yoshitaka Ushiku(Univ. of Tokyo) / Tomoharu Iwata(NTT) / Shigeyuki Oba(Kyoto Univ.) |
| Paper Information | |
| Registration To | Technical Committee on Pattern Recognition and Media Understanding / Technical Committee on Infomation-Based Induction Sciences and Machine Learning / Special Interest Group on Computer Vision and Image Media |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Arrangement of Complementary Weak Learners using Weights Assigned to Data in Parallel Ensemble Learning |
| Sub Title (in English) | |
| Keyword(1) | Ensemble Learning |
| Keyword(2) | Boosting |
| Keyword(3) | AdaBoost |
| Keyword(4) | Error Correcting Output Codes |
| Keyword(5) | Multi-Class Classification |
| 1st Author's Name | Shota Utsumi |
| 1st Author's Affiliation | University of Tsukuba(Univ. of Tsukuba) |
| 2nd Author's Name | Keisuke Kameyama |
| 2nd Author's Affiliation | University of Tsukuba(Univ. of Tsukuba) |
| Date | 2018-09-20 |
| Paper # | PRMU2018-37,IBISML2018-14 |
| Volume (vol) | vol.118 |
| Number (no) | PRMU-219,IBISML-220 |
| Page | pp.pp.9-15(PRMU), pp.9-15(IBISML), |
| #Pages | 7 |
| Date of Issue | 2018-09-13 (PRMU, IBISML) |