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让贝斯睡觉Putting Bayes to sleep |
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| 课程网址: | http://videolectures.net/machine_koolen_bayes/ |
| 主讲教师: | Wouter M. Koolen |
| 开课单位: | 中华科技中心公司 |
| 开课时间: | 2013-06-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We consider sequential prediction algorithms that are given the predictions from a set of models as inputs. If the nature of the data is changing over time in that different models predict well on different segments of the data, then adaptivity is typically achieved by mixing into the weights in each round a bit of the initial prior (kind of like a weak restart). However, what if the favored models in each segment are from a small subset, i.e. the data is likely to be predicted well by models that predicted well before? Curiously, fitting such ''sparse composite models'' is achieved by mixing in a bit of all the past posteriors. This self-referential updating method is rather peculiar, but it is efficient and gives superior performance on many natural data sets. Also it is important because it introduces a long-term memory: any model that has done well in the past can be recovered quickly. While Bayesian interpretations can be found for mixing in a bit of the initial prior, no Bayesian interpretation is known for mixing in past posteriors. We build atop the ''specialist'' framework from the online learning literature to give the Mixing Past Posteriors update a proper Bayesian foundation. We apply our method to a well-studied multitask learning problem and obtain a new intriguing efficient update that achieves a significantly better bound. |
| 关 键 词: | 顺序预测; 弱启动; 权重混合 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-05-15：cwx |
| 阅读次数: | 72 |