·822· 智能系统学报 第12卷 al conference on Web search and data mining.Seattle,USA. using forward backward splitting[J].Journal of Machine 2012:133-142. Learning Research,2009,10(12):2899-2934. [5]TIBSHIRANI R.Regression shrinkage and selection via the [12]RENDLE S.Factorization machines with libfm[J].ACM lasso[J].Journal of the royal statistical society,Series B transactions on intelligent systems and technolog,2012, (Methodological).1996.73(3):267-288. 33):57. [6]YUAN M,LIN Y.Model selection and estimation in regres- [13]LIU J,YE J.Moreau-Yosida regularization for grouped sion with grouped variables[J].Journal of the Royal Statist- tree structure learning[Cl//Advances in Neural Information ical Society:Series B(Statistical Methodology),2006, Processing Systems.Vancouver,Canada,2010:1459- 68(1):49-67 1467. [7]SIMON N,FRIEDMAN J,HASTIE T,et al.A sparse-group 作者简介： lasso[J].Journal of computational and graphical statistics, 郭少成，男，1993年生，硕士研究 2013,22(2):231-245. 生，主要研究方向为机器学习、模式 [8]BLONDEL M,FUJINO A,UEDA N,et al.Higher-order 识别。 factorization machines[C//Advances in Neural Information Processing Systems.Barcelona,Spain 2016:3351-3359. [9]LI M,LIU Z,SMOLA A J,et al.DiFacto:distributed factor- ization machines[C]//Proceedings of the Ninth ACM Inter- national Conference on Web Search and Data Mining.San 陈松灿.男，1962年生.教授，博 Francisco,USA,2016:377-386. 士生导师，博士，中国人工智能学会机 器学习专委会主任，CC℉高级会员，主 [10]CHIN WS,YUAN B,YANG M Y,et al.An efficient al- 要研究方向为模式识别、机器学习、神 ternating newton method for learning factorization machines 经计算。在国际主流期刊和顶级会议 [R].NTU:NTU,2016. 上发表多篇学术论文并多次获奖。 [11]DUCHI J,SINGER Y.Efficient online and batch learningal conference on Web search and data mining. Seattle, USA, 2012: 133–142. TIBSHIRANI R. Regression shrinkage and selection via the lasso[J]. Journal of the royal statistical society, Series B (Methodological), 1996, 73(3): 267–288. [5] YUAN M, LIN Y. Model selection and estimation in regres￾sion with grouped variables[J]. Journal of the Royal Statist￾ical Society: Series B (Statistical Methodology), 2006, 68(1): 49–67. [6] SIMON N, FRIEDMAN J, HASTIE T, et al. A sparse-group lasso[J]. Journal of computational and graphical statistics, 2013, 22(2): 231–245. [7] BLONDEL M, FUJINO A, UEDA N, et al. Higher-order factorization machines[C]//Advances in Neural Information Processing Systems. Barcelona, Spain 2016: 3351–3359. [8] LI M, LIU Z, SMOLA A J, et al. DiFacto: distributed factor￾ization machines[C]//Proceedings of the Ninth ACM Inter￾national Conference on Web Search and Data Mining. San Francisco, USA, 2016: 377–386. [9] CHIN W S, YUAN B, YANG M Y, et al. An efficient al￾ternating newton method for learning factorization machines [R].NTU:NTU,2016. [10] [11] DUCHI J, SINGER Y. Efficient online and batch learning using forward backward splitting[J]. Journal of Machine Learning Research, 2009, 10(12): 2899–2934. RENDLE S. Factorization machines with libfm[J]. ACM transactions on intelligent systems and technolog, 2012, 3(3): 57. [12] LIU J, YE J. Moreau-Yosida regularization for grouped tree structure learning[C]//Advances in Neural Information Processing Systems. Vancouver, Canada, 2010: 1459– 1467. [13] 作者简介： 郭少成，男，1993 年生，硕士研究 生，主要研究方向为机器学习、模式 识别。 陈松灿，男，1962 年生，教授，博 士生导师，博士，中国人工智能学会机 器学习专委会主任，CCF 高级会员，主 要研究方向为模式识别、机器学习、神 经计算。在国际主流期刊和顶级会议 上发表多篇学术论文并多次获奖。 ·822· 智 能 系 统 学 报 第 12 卷