图 书 馆
数据分类：稀疏性的连续方法Datum-Wise Classification: A Sequential Approach to Sparsity |
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| 课程网址: | http://videolectures.net/ecmlpkdd2011_denoyer_sparsity/ |
| 主讲教师: | Ludovic Denoyer |
| 开课单位: | 皮埃尔和玛丽·居里大学 |
| 开课时间: | 2011-11-30 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose a novel classification technique whose aim is to select an appropriate representation for each datapoint, in contrast to the usual approach of selecting a representation encompassing the whole dataset. This datum-wise representation is found by using a sparsity inducing empirical risk, which is a relaxation of the standard L0 regularized risk. The classification problem is modeled as a sequential decision process that sequentially chooses, for each datapoint, which features to use before classifying. Datum-Wise Classification extends naturally to multi-class tasks, and we describe a specific case where our inference has equivalent complexity to a traditional linear classifier, while still using a variable number of features. We compare our classifier to classical L1 regularized linear models (L1-SVM and LARS) on a set of common binary and multi-class datasets and show that for an equal average number of features used we can get improved performance using our method. |
| 关 键 词: | 监督学习; 稀疏的顺序的方法; 主要途径稀疏 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-24：yumf |
| 阅读次数: | 34 |