图 书 馆
木兰木兰Mulan Mulan |
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| 课程网址: | http://videolectures.net/icml2010_spyromitros_mumu/ |
| 主讲教师: | Eleftherios Spyromitros - Xioufis |
| 开课单位: | 塞萨洛尼基亚里士多德大学 |
| 开课时间: | 2010-07-20 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Mulan is an open-source Java library for learning from multi-label datasets. Multi-label datasets consist of training examples of a target function that has multiple binary target variables. This means that each item of a multi-label dataset can be a member of multiple categories or annotated by many labels (classes). This is actually the nature of many real world problems such as semantic annotation of images and video, web page categorization, direct marketing, functional genomics and music categorization into genres and emotions. An introduction on mining multi-label data is provided in (Tsoumakas et al., 2010). Currently, the library includes a variety of state-of-the-art algorithms for performing the following major multi-label learning tasks: Classification. This task is concerned with outputting a bipartition of the labels into relevant and irrelevant ones for a given input instance. Ranking. This task is concerned with outputting an ordering of the labels, according to their relevance for a given data item Classification and ranking. A combination of the two tasks mentioned-above. In addition, the library offers the following features: Feature selection. Simple baseline methods are currently supported. Evaluation. Classes that calculate a large variety of evaluation measures through hold-out evaluation and cross-validation. As already mentioned, Mulan is a library. As such, it offers only programmatic API to the library users. There is no graphical user interface (GUI) available. The possibility to use the library via command line, is also currently not supported. The Getting Started page in the Documentation section is the ideal place to start exploring Mulan. |
| 关 键 词: | 数据集学习; 功能选择; 基线方法 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2022-12-19：chenjy |
| 最后编审: | 2023-05-11：chenjy |
| 阅读次数: | 54 |