图 书 馆
推进基础Classifiers产品分类Boosting Products of Base Classifiers |
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| 课程网址: | http://videolectures.net/icml09_kegl_bpbc/ |
| 主讲教师: | Balazs Kegl |
| 开课单位: | 巴黎大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In this paper we show how to boost products of simple base learners. Similarly to trees, we call the base learner as a subroutine but in an iterative rather than recursive fashion. The main advantage of the proposed method is its simplicity and computational efficiency. On benchmark datasets, our boosted products of decision stumps clearly outperform boosted trees, and on the MNIST dataset the algorithm achieves the second best result among no-domain-knowledge algorithms after deep belief nets. As a second contribution, we present an improved base learner for nominal features and show that boosting the product of two of these new subset indicator base learners solves the maximum margin matrix factorization problem used to formalize the collaborative filtering task. On a small benchmark dataset, we get experimental results comparable to the semi-definite-programming-based solution but at a much lower computational cost. |
| 关 键 词: | 基准数据集; 名义特征; 子集指标; 有限编程 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-07：lxf |
| 阅读次数: | 49 |