图 书 馆
计算广告中的概率机器学习Probabilistic Machine Learning in Computational Advertising |
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| 课程网址: | http://videolectures.net/nipsworkshops09_graepel_pmlca/ |
| 主讲教师: | Thore Graepel |
| 开课单位: | 微软公司 |
| 开课时间: | 2010-01-19 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In the past years online advertising has grown at least an order of magnitude faster than advertising on all other media. This talk focuses on advertising on search engines, where accurate predictions of the probability that a user clicks on an advertisement crucially benefit all three parties involved: the user, the advertiser, and the search engine. We present a Bayesian probabilistic classification model that has the ability to learn from terabytes of web usage data. The model explicitly represents uncertainty allowing for fully probabilistic predictions: 2 positives out of 10 instances or 200 out of 1000 both give an average of 20%, but in the first case the uncertainty about the prediction should be larger. We also present a scheme for approximate parallel inference that allows efficient training of the algorithm on a distributed data architecture. |
| 关 键 词: | 在线广告; 搜索引擎; 贝叶斯模型; 不确定性预测; 分布式数据架构 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-08：吴雨秋(课程编辑志愿者) |
| 阅读次数: | 54 |