一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機號碼
標(biāo)題
留言內(nèi)容
驗證碼

v-軟間隔羅杰斯特回歸分類機

黃成泉,  王士同,  蔣亦樟,  董愛美

黃成泉, 王士同, 蔣亦樟, 董愛美. v-軟間隔羅杰斯特回歸分類機[J]. 電子與信息學(xué)報, 2016, 38(4): 985-992. doi: 10.11999/JEIT150769
引用本文: 黃成泉, 王士同, 蔣亦樟, 董愛美. v-軟間隔羅杰斯特回歸分類機[J]. 電子與信息學(xué)報, 2016, 38(4): 985-992. doi: 10.11999/JEIT150769
HUANG Chengquan, WANG Shitong, JIANG Yizhang, DONG Aimei. v-Soft Margin Logistic Regression Classifier[J]. Journal of Electronics & Information Technology, 2016, 38(4): 985-992. doi: 10.11999/JEIT150769
Citation: HUANG Chengquan, WANG Shitong, JIANG Yizhang, DONG Aimei. v-Soft Margin Logistic Regression Classifier[J]. Journal of Electronics & Information Technology, 2016, 38(4): 985-992. doi: 10.11999/JEIT150769

v-軟間隔羅杰斯特回歸分類機

doi: 10.11999/JEIT150769 cstr: 32379.14.JEIT150769
  • 2.

    (江南大學(xué)數(shù)字媒體學(xué)院 無錫 214122) ②(貴州民族大學(xué)工程實訓(xùn)中心 貴陽 550025) ③(齊魯工業(yè)大學(xué)信息學(xué)院 濟南 250353)

基金項目: 

國家自然科學(xué)基金(61272210, 61202311)，江蘇省自然科學(xué)基金(BK2012552)，貴州省科學(xué)技術(shù)基金(黔科合J字[2013]2136號)

v-Soft Margin Logistic Regression Classifier

  • 2.

    (School of Digital Media, Jiangnan University, Wuxi 214122, China)

Funds: 

The National Natural Science Foundation of China (61272210, 61202311), The Natural Science Foundation of Jiangsu Province (BK2012552), The Science and Technology Foundation of Guizhou Province ([2013]2136)

  • 摘要: 坐標(biāo)下降(Coordinate Descent, CD)方法是求解大規(guī)模數(shù)據(jù)分類問題的有效方法，具有簡單操作流程和快速收斂速率。為了提高羅杰斯特回歸分類器(Logistic Regression Classifier, LRC)的泛化性能，受v-軟間隔支持向量機的啟發(fā)，該文提出一種v-軟間隔羅杰斯特回歸分類機(v-Soft Margin Logistic Regression Classifier, v-SMLRC)，證明了v-SMLRC對偶為一等式約束對偶坐標(biāo)下降CDdual并由此提出了適合于大規(guī)模數(shù)據(jù)的v-SMLRC-CDdual。所提出的v-SMLRC-CDdual既能最大化類間間隔，又能有效提高LRC的泛化性能。大規(guī)模文本數(shù)據(jù)集實驗表明，v-SMLRC-CDdual分類性能優(yōu)于或等同于相關(guān)方法。
    Abstract: Coordinate Descent (CD) is a promising method for large scale pattern classification issues with straightforward operation and fast convergence speed. In this paper, inspired by v-soft margin Support Vector Machine (v-SVM) for pattern classification, a new v-Soft Margin Logistic Regression Classifier (v-SMLRC) is proposed for pattern classification to enhance the generalization performance of Logistic Regression Classifier (LRC). The dual of v-SMLRC can be regarded as CDdual problem with equality constraint and then a new large scale pattern classification method called v-SMLRC-CDdual is proposed. The proposed v-SMLRC-CDdual can maximize the inner-class margin and effectively enhance the generalization performance of LRC. Empirical results conducted with large scale document datasets demonstrate that the proposed method is effective and comparable to other related methods.
    • HTML全文
  • BOTTOU L and BOUSQUET O. The tradeoffs of large scale learning[C]. Proceedings of Advances in Neural Information Processing Systems, Cambridge, 2008: 151-154.
    LIN C Y, TSAI C H, LEE C P, et al. Large-scale logistic regression and linear support vector machines using Spark[C]. Proceedings of 2014 IEEE International Conference on Big Data, Washington DC, 2014: 519-528. doi: 10.1109/BigData. 2014.7004269.
    AGERRI R, ARTOLA X, BELOKI Z, et al. Big data for natural language processing: A streaming approach[J]. Knowledge-Based Systems, 2015, 79: 36-42. doi: 10.1016/ j.knosys.2014.11.007.
    DARROCH J N and RATCLIFF D. Generalized iterative scaling for log-linear models[J]. The Annals of Mathematical Statistics, 1972, 43(5): 1470-1480. doi: 10.1214/aoms/ 1177692379.
    DELLA P S, DELLA P V, and LAFFERTY J. Inducing features of random fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(4): 380-393. doi: 10.1109/34.588021.
    GOODMAN J. Sequential conditional generalized iterative scaling[C]. Proceedings of the 40th annual meeting of the association of computational linguistics, Philadelphia, 2002: 9-16. doi: 10.3115/1073083.1073086.
    JIN R, YAN R, ZHANG J, et al. A faster iterative scaling algorithm for conditional exponential model[C]. Proceedings of the 20th International Conference on Machine Learning, New York, 2003: 282-289.
    HUANG F L, HSIEN C J, CHANG K W, et al. Iterative scaling and coordinate descent methods for maximum entropy[J]. Journal of Machine Learning Research, 2010, 11(2): 815-848.
    KOMAREK P and MOORE A W. Making logistic regression a core data mining tool: a practical investigation of accuracy, speed, and simplicity[R]. Technical report TR-05-27, Robotics Institute of Carnegie Mellon University, Pittsburgh, 2005.
    LIN C J, WENG R C, and KEERTHI S S. Trust region Newton method for large-scale logistic regression[J]. Journal of Machine Learning Research, 2008, 9(4): 627-650.
    KEERTHI S S, DUAN K B, SHEVADE S K, et al. A fast dual algorithm for kernel logistic regression[J]. Machine Learning, 2005, 61(1-3): 151-165. doi: 10.1007/s10994- 005-0768-5.
    PLATT J C. Fast training of support vector machines using sequential minimal optimization[C]. Proceedings of Advances in Kernel Methods: Support Vector Learning, Cambridge, 1999: 185-208.
    YU H F, HUANG F L, and LIN C J. Dual coordinate descent methods for logistic regression and maximum entropy models[J]. Machine Learning, 2011, 85(1/2): 41-75. doi: 10.1007/s10994-010-5221-8.
    顧鑫, 王士同, 許敏. 基于多源的跨領(lǐng)域數(shù)據(jù)分類快速新算法[J]. 自動化學(xué)報, 2014, 40(3): 531-547. doi: 10.3724/SP.J. 1004.2014.00531.
    GU X, WANG S T, and XU M. A new cross-multidomain classification algorithm and its fast version for large datasets[J]. Acta Automatica Sinica, 2014, 40(3): 531-547. doi: 10.3724/SP.J.1004.2014.00531.
    顧鑫, 王士同. 大樣本多源域與小目標(biāo)域的跨領(lǐng)域快速分類學(xué)習(xí)[J]. 計算機研究與發(fā)展, 2014, 51(3): 519-535. doi: 10.7544/issn1000-1239.2014.20120652.
    GU X and WANG S T. Fast cross-domain classification method for large multisources/small target domains[J]. Journal of Computer Research and Development, 2014, 51(3): 519-535. doi: 10.7544/issn1000-1239.2014.20120652.
    張學(xué)峰, 陳渤, 王鵬輝, 等. 一種基于Dirichelt過程隱變量支撐向量機模型的目標(biāo)識別方法[J]. 電子與信息學(xué)報, 2015, 37(1): 29-36. doi: 10.11999/JEIT140129.
    ZHANG X F, CHEN B, WANG P H, et al. A target recognition method based on dirichlet process latent variable support vector machine model[J]. Journal of Electronics Information Technology, 2015, 37(1): 29-36. doi: 10.11999/ JEIT140129.
    及歆榮, 侯翠琴, 侯義斌. 無線傳感器網(wǎng)絡(luò)下線性支持向量機分布式協(xié)同訓(xùn)練方法研究[J]. 電子與信息學(xué)報, 2015, 37(3): 708-714. doi: 10.11999/JEIT140408.
    JI X R, HOU C Q, and HOU Y B. Research on the distributed training method for linear SVM in WSN[J]. Journal of Electronics Information Technology, 2015, 37(3): 708-714. doi: 10.11999/JEIT140408.
    高發(fā)榮, 王佳佳, 席旭剛, 等. 基于粒子群優(yōu)化-支持向量機方法的下肢肌電信號步態(tài)識別[J]. 電子與信息學(xué)報, 2015, 37(5): 1154-1159. doi: 10.11999/JEIT141083.
    GAO F R, WANG J J, XI X G, et al. Gait recognition for lower extremity electromyographic signals based on PSO- SVM method[J]. Journal of Electronics Information Technology, 2015, 37(5): 1154-1159. doi: 10.11999/ JEIT141083.
    HSIEH C J, CHANG K W, LIN C J, et al. A dual coordinate descent method for large-scale linear SVM[C]. Proceedings of the 25th International Conference on Machine Learning, New York, 2008: 408-415. doi: 10.1145/1390156.1390208.
    CHEN P H, LIN C J, and SCHLKOPF B. A tutorial on v-support vector machines[J]. Applied Stochastic Models in Business and Industry, 2005, 21(2): 111-136. doi: 10.1002/ asmb.537.
    PENG X J, CHEN D J, and KONG L Y. A clipping dual coordinate descent algorithm for solving support vector machines[J]. Knowledge-Based Systems, 2014, 71: 266-278. doi: 10.1016/j.knosys.2014.08.005.
    TSAI C H, LIN C Y, and LIN C J. Incremental and decremental training for linear classification[C]. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2014: 343-352. doi: 10.1145/2623330.2623661.
  • 加載中
計量
  • 文章訪問數(shù):  1848
  • HTML全文瀏覽量:  120
  • PDF下載量:  442
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2015-06-29
  • 修回日期:  2015-12-08
  • 刊出日期:  2016-04-19

目錄

    /

    返回文章
    返回