图 书 馆
环境与生命科学的机器学习Machine learning for environmental and life sciences |
|
| 课程网址: | http://videolectures.net/nib_dzeroski_machine_learning/ |
| 主讲教师: | Sašo Džeroski |
| 开课单位: | 约瑟夫·斯特凡学院 |
| 开课时间: | 2019-03-27 |
| 课程语种: | 英语 |
| 中文简介: |  我们越来越需要从大数据或复杂数据中学习预测模型，其中可能包含许多示例和许多输入/输出维度。当必须预测多个目标变量时，我们将讨论多目标预测。预测建模问题也可能以其他方式复杂化，例如，在半监督学习中，它们可能涉及不完整/部分标记的数据。演讲首先将介绍多目标预测的不同任务，例如多目标分类和回归，其分层版本以及涉及额外复杂性的任务版本（例如半监督多目标回归）。它将继续提出解决这些任务的一些方法。最后，它将回顾多目标预测在环境和生命科学中的不同应用，从相关的环境条件和生物群组成，到基因功能预测，再到用于药物复性的虚拟化合物筛选的预测模型 |
| 课程简介: | Increasingly often, we need to learn predictive models from big or complex data, which may comprise many examples and many input/output dimensions. When more than one target variable has to be predicted, we talk about multi-target prediction. Predictive modeling problems may also be complex in other ways, e.g., they may involve incompletely/partially labelled data, as in semi-supervised learning. The talk will first give an introduction to the different tasks of multi-target prediction, such as multi-target classification and regression, hierarchical versions thereof, and versions of the tasks that involve additional complexity (such as semi-supervised multi-target regression). It will continue to present some methods for solving such tasks. Finally, it will review different applications of multi-target prediction in environmental and life sciences, ranging from relating environmental conditions and the composition of biota, through gene function prediction, to predictive modeling in virtual compound screening for drug repurposing |
| 关 键 词: | 多目标分类; 生命科学; 基因功能预测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-23：cjy |
| 最后编审: | 2021-01-15：yumf |
| 阅读次数: | 23 |