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baroshi木虫 (正式写手)
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[求助]
帮忙查一篇文章的SCI号，谢谢。
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Selective multiple kernel learning for classification with ensemble strategy [ 发自手机版 http://muchong.com/3g ] |
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muse
捐助贵宾 (知名作家)
- LS-EPI: 147
- 应助: 314 (大学生)
- 贵宾: 0.549
- 金币: 20674
- 散金: 2783
- 红花: 81
- 沙发: 156
- 帖子: 6098
- 在线: 1468.2小时
- 虫号: 1207111
- 注册: 2011-02-19
- 专业: 保险学
4楼2014-09-21 18:51:13
muse
捐助贵宾 (知名作家)
- LS-EPI: 147
- 应助: 314 (大学生)
- 贵宾: 0.549
- 金币: 20674
- 散金: 2783
- 红花: 81
- 沙发: 156
- 帖子: 6098
- 在线: 1468.2小时
- 虫号: 1207111
- 注册: 2011-02-19
- 专业: 保险学
【答案】应助回帖
感谢参与，应助指数 +1
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Selective multiple kernel learning for classification with ensemble strategy 作者:Tao Sun; Licheng Jiao; Fang Liu; Shuang Wang; Jie Feng Pattern Recognition 卷: 46 期: 11 页: 3081-90 DOI: 10.1016/j.patcog.2013.04.003 出版年: Nov. 2013 摘要 Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels. However, MKL with L1-norm easily discards the sub-kernels with complementary information and MKL with Lp-norm(pges2) often gets the redundant solution. To address these problems, a Selective Multiple Kernel Learning (SMKL) method, inspired by Ensemble Learning (EL), is proposed. Comparing MKL with Lp-norm(pges2), SMKL obtains a sparse solution by a pre-selection procedure. Comparing MKL with L1-norm, SMKL preserves the sub-kernels with complementary information by guaranteeing the high discrimination and large diversity of pre-selected sub-kernels. For quantifying the discrimination and diversity of sub-kernels, a new kernel evaluation is designed. SMKL reduces the scale of MKL optimization and saves the memory storing of the sub-kernels, which extends the scale of problem that MKL could solve. Specially, a fast SMKL method using Linfinity-norm constraint is focused, which needs no MKL optimization process. It means that the memory is hardly a limitation for MKL with the large scale problem. Experiments state that our method is effective for classification. [All rights reserved Elsevier]. 作者信息 作者地址: Tao Sun; Licheng Jiao; Shuang Wang; Jie Feng; Key Lab. of Intell. Perception & Image Understanding of Minist. of Educ. of China, Xidian Univ., Xi'an, China. Fang Liu; Sch. of Comput. Sci. & Technol., Xidian Univ., Xi'an, China. 出版商 Elsevier Science Ltd., UK 类别 / 分类 研究方向:Computer Science; Mathematics (由 Thomson Reuters 提供) 分类代码:C1230L Learning in AI; C1180 Optimisation techniques CODEN  TNRA8受控索引:learning (artificial intelligence); optimisation; pattern classification 非受控索引:selective multiple kernel learning; ensemble strategy classificatio; single kernel learning; ensemble learning; EL; preselection procedure; Linfinity-norm constraint; SMKL method; MKL optimization process 文献信息 文献类型:Journal Paper 语种:English 入藏号:13704917 ISSN:0031-3203 参考文献数:32 期刊信息 Impact Factor (影响因子): Journal Citation Reports® 其他信息 处理类型:Theoretical or Mathematical 文献号:S0031-3203(13)00173-8 |
2楼2014-09-21 18:50:40
muse
捐助贵宾 (知名作家)
- LS-EPI: 147
- 应助: 314 (大学生)
- 贵宾: 0.549
- 金币: 20674
- 散金: 2783
- 红花: 81
- 沙发: 156
- 帖子: 6098
- 在线: 1468.2小时
- 虫号: 1207111
- 注册: 2011-02-19
- 专业: 保险学
【答案】应助回帖
★ ★ ★ ★ ★
baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 18:56:25
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-21 19:36:28
baroshi: 金币+5, ★★★★★最佳答案 2014-09-21 18:56:25
sunshan4379: LS-EPI+1, 感谢应助！ 2014-09-21 19:36:28
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Selective multiple kernel learning for classification with ensemble strategy 作者:Sun, T (Sun, Tao)[ 1 ] ; Jiao, LC (Jiao, Licheng)[ 1 ] ; Liu, F (Liu, Fang)[ 2 ] ; Wang, S (Wang, Shuang)[ 1 ] ; Feng, J (Feng, Jie)[ 1 ] PATTERN RECOGNITION 卷: 46 期: 11 页: 3081-3090 DOI: 10.1016/j.patcog.2013.04.003 出版年: NOV 2013 查看期刊信息 摘要 Multiple Kernel Learning (MKL) aims to seek a better result than single kernel learning by combining a compact set of sub-kernels. However, MKL. with L1-norm easily discards the sub-kernels with complementary information and MKL with Lp-norm(p >= 2) often gets the redundant solution. To address these problems, a Selective Multiple Kernel Learning (SMKL) method, inspired by Ensemble Learning (EL), is proposed. Comparing MKL with Lp-norm(p >= 2), SMKL obtains a sparse solution by a pre-selection procedure. Comparing MKL with Lp-norm, SMKL preserves the sub-kernels with complementary information by guaranteeing the high discrimination and large diversity of pre-selected sub-kernels. For quantifying the discrimination and diversity of sub-kernels, a new kernel evaluation is designed. SMKL reduces the scale of MKL optimization and saves the memory storing of the sub-kernels, which extends the scale of problem that MKL could solve. Specially, a fast SMKL method using L infinity-norm constraint is focused, which needs no MIC optimization process. It means that the memory is hardly a limitation for MKL with the large scale problem. Experiments state that our method is effective for classification. (C) 2013 Elsevier Ltd. All rights reserved. 关键词 作者关键词:Ensemble learning; Kernel evaluation; Multiple kernel learning; Selective multiple kernel learning; Fast selective multiple kernel learning KeyWords Plus  IMENSIONALITY REDUCTION作者信息 通讯作者地址: Feng, J (通讯作者) [显示增强组织信息的名称] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China. 地址: [显示增强组织信息的名称] [ 1 ] Xidian Univ, Minist Educ China, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China [显示增强组织信息的名称] [ 2 ] Xidian Univ, Sch Comp Sci & Technol, Xian 710071, Peoples R China 电子邮件地址:taosun@mail.xidian.edu.cn; lchjiao@mail.xidian.edu.cn; f63liu@163.com; shwang@mail.xidian.edu.cn; jiefeng0109@163.com 基金资助致谢 基金资助机构 授权号 National Basic Research Program (973 Program) of China 2013CB329402 National Natural Science Foundation of China 61173092 61072106 61003198 Program for New Century Excellent Talents in University NCET-11-0692 Fundamental Research Funds for the Central Universities K50510020001 K50513100012 查看基金资助信息 出版商 ELSEVIER SCI LTD, THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 类别 / 分类 研究方向:Computer Science; Engineering Web of Science 类别:Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic 文献信息 文献类型:Article 语种:English 入藏号: WOS:000321232900017 ISSN: 0031-3203 期刊信息 目录： Current Contents Connect® Impact Factor (影响因子): Journal Citation Reports® 其他信息 IDS 号: 175LT Web of Science 核心合集中的 "引用的参考文献": 32 Web of Science 核心合集中的 "被引频次": 2 |
3楼2014-09-21 18:51:05
