Boosting: Foundations and Al- gorithms. The MIT Press, 2012.Schapire, R.E., Freund, Y.: Boosting: Foundations and algorithms. MIT press (2012).R. Schapire and Y. Freund. Boosting: Foundations and Algorithms. MIT Press, 2012R. E. Schapire and Y. Freund, Boosting: Foundations and ...
副标题: Foundations and Algorithms出版年: 2012-5-18页数: 544定价: USD 57.00装帧: Hardcover丛书: Adaptive Computation and Machine LearningISBN: 9780262017183豆瓣评分 9.2 14人评价 5星 57.1% 4星 35.7% 3星 7.1% 2星 0.0% 1星 0.0% 评价:
内容所属专栏 大规模机器学习与集成学习 向量化、多核化、GPU、集群等,《Ensemble Methods: Foundations and Algorithms》翻译等 订阅专栏 ensemble 机器学习 boosting 赞同5添加评论 分享喜欢收藏申请转载 写下你的评论... 还没有评论,发表第一个评论吧 推荐阅读 Ensemble Method : ...
Schapire & Freund. Boosting: Foundations and Algorithms. MIT. He et al.Deep Residual Learning for Image Recognition. Veit et al. Aggregated Residual Transformations for Deep Neural Networks.
[2] Schapire R E, Freund Y.Boosting: Foundations and algorithms[M]. MIT press, 2012. [3] Breiman L. Stackedregressions[J]. Machine learning, 1996, 24(1): 49-64. 想关注更多有趣的机器学习/计算物理/医疗影像的科普/前沿研究,请关注我们的公众号 ...
我想掌握这些结论(或者常识)是不够,还需要知道Why,因此在结合西瓜书和《Ensemble Mothods: Foundations and Algorithms》以及FHT00(ADDITIVE LOGISTIC REGRESSION: A STATISTICAL VIEW OF BOOSTING)资料之后,发现对于AdaBoost的研究与解释,如AdaBoost能够对抗过拟合问题(不断加大基模型个数,在测试集上范化误差居然可以一直...
Schapire & Freund. Boosting: Foundations and Algorithms. MIT. He et al.Deep Residual Learning for Image Recognition. Veit et al.Residual Networks Behave Like Ensembles of Relatively Shallow Networks. Xie et al.Aggregated Residual Transformations for Deep Neural Networks....
(2012). Boosting: Foundations and algorithms. Cambridge: MIT Press. Google Scholar Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine learning, 37(3), 297–336. Article MATH Google Scholar Schölkopf, B., & Smola, A. J. (...
关于同一逻辑的更为技术性的引用写在Probably Approximately Correct: Nature’s Algorithms for Learning and Prospering in a Complex World,“这个想法是多次使用弱的学习方法来获得连续的假设,每一个调整的例子是以往发现困难和错误分类的。...但是,请注意,能做到这一点并不明显”。
foundations and algorithms . cambridge: mit press. math google scholar schölkopf, b., herbrich, r., & smola, a. j. (2001). a generalized representer theorem. neural networks and computational learning theory , 81 , 416–426. mathscinet math google scholar ...