|
用于筛选数据分析的半监督多目标预测
Semi-supervised multi-target prediction for analysis of screening data |
|
| |
| 课程网址: |
http://videolectures.net/icgeb_kocev_screening_data/
|
| 主讲教师: |
Dragi Kocev
|
| 开课单位: |
Joíef Stefan学院知识技术部
|
| 开课时间: |
2019-06-28
|
| 课程语种: |
英语
|
| 中文简介: |
传统监督方法的预测性能在很大程度上取决于标记数据的数量。然而，在许多实际任务中，获取标签是一个困难的过程，包括化合物筛选、生物标记物发现等。通常只有少量标记数据可用于模型学习。作为对这个问题的回答，半监督学习的概念应运而生。半监督方法除了使用标记数据外，还使用未标记数据来提高监督方法的性能。对于结构化输出的数据挖掘问题，获取带标签的数据更加困难，因为需要为每个示例确定多个标签。多目标预测（MTP）是一种结构化输出预测问题，需要同时预测多个变量。尽管表面上需要能够处理MTP的半监督方法，但只有少数此类方法可用，甚至那些在实践中难以使用的方法和/或其相对于监督方法的MTP优势尚不明确。我们将提出一种从有限数量的标记数据中学习预测模型的算法，该算法可以利用可用的未标记数据，以获得具有更好预测性能的模型。我们还将展示一些基准实验，以评估其预测性能。最后，我们将说明并讨论它们在高内容屏幕分析中的用途。
|
| 课程简介: |
The predictive performance of traditional supervised methods heavily depends on the amount of labeled data. However, obtaining labels is a difficult process in many real-life tasks including compound screens, biomarker discovery etc. Only a small amount of labeled data is typically available for model learning. As an answer to this problem, the concept of semi-supervised learning has emerged. Semi-supervised methods use unlabeled data in addition to labeled data to improve the performance of supervised methods. It is even more difficult to get labeled data for data mining problems with structured outputs since several labels need to be determined for each example. Multi-target prediction (MTP) is one type of a structured output prediction problem, where we need to simultaneously predict multiple variables. Despite the apparent need for semi-supervised methods able to deal with MTP, only a few such methods are available and even those are difficult to use in practice and/or their advantages over supervised methods for MTP are not clear. We will present an algorithm for learning predictive models from limited amount of labelled data that can exploit the available unlabelled data in a way to yield models with better predictive performance. We will also show some benchmark experiments to assess their predictive performance. Finally, we will illustrate and discuss their use for analysis of high content screens.
|
| 关 键 词: |
筛选数据分析; 半监督多目标预测; MTP的半监督方法; 模型学习
|
| 课程来源: |
视频讲座网
|
| 数据采集: |
2022-10-14：cyh
|
| 最后编审: |
2022-10-14：cyh
|
| 阅读次数: |
38
|
|
图 书 馆