图 书 馆
预测结构化输出的集合Ensembles for predicting structured outputs |
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| 课程网址: | http://videolectures.net/solomon_kocev_epso/ |
| 主讲教师: | Dragi Kocev |
| 开课单位: | 约瑟夫·斯特凡学院 |
| 开课时间: | 2010-02-16 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | In many real-world domains, such as bioinformatics (functional genomics), text classification and image annotation, the goal is to predict a complex output. For example, in functional genomics, the goal is to predict the function of a gene, while the set of functions can be organized as tree (FunCat) or graph (GO ontology). In this talk, we present an approach for predicting structured outputs using ensembles of trees. The proposed approach is scalable to large datasets, different types of outputs and it is applicable to wide range of domains. First, we describe the types of structured outputs that we typically encounter, and then we explain the base classifiers - predictive clustering trees (PCTs). Next, we discuss the ensemble methods that we extended (bagging and random forests) to deal with structured outputs and accordingly adapted the voting schemes. Afterwards, we present experimental evaluation of the proposed approach on wide range of real-world domains. At the end, we present an application of the proposed approach in functional genomics and show that our approach is competitive with state-of-the-art approaches. |
| 关 键 词: | 功能基因组学; 预测基因; 预测聚类树 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-22：cwx |
| 阅读次数: | 35 |