图 书 馆
基于非参数学习预测子空间的多任务学习Multitask Learning Using Nonparametrically Learned Predictor Subspaces |
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| 课程网址: | http://videolectures.net/nipsworkshops09_rai_mlun/ |
| 主讲教师: | Piyush Rai |
| 开课单位: | 犹他大学 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Given several related learning tasks, we propose a nonparametric Bayesian learning model that captures task relatedness by assuming that the task parameters (i.e., weight vectors) share a latent subspace. More specifically, the intrinsic dimensionality of this subspace is not assumed to be known a priori. We use an infinite latent feature model - the Indian Buffet Process - to automatically infer this number. We also propose extensions of this model where the subspace learning can incorporate (labeled, and additionally unlabeled if available) examples, or the task parameters share a mixture of subspaces, instead of sharing a single subspace. The latter property can allow learning nonlinear manifold structure underlying the task parameters, and can also help in preventing negative transfer from outlier tasks. |
| 关 键 词: | 非参数贝叶斯; 权重向量; 非线性流形结构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-07：lxf |
| 阅读次数: | 89 |