|
不完全信息的提高
Boosting with Incomplete Information |
|
| |
| 课程网址: |
http://videolectures.net/icml08_wang_bii/
|
| 主讲教师: |
Yang Wang
|
| 开课单位: |
西蒙弗雷泽大学
|
| 开课时间: |
2008-08-06
|
| 课程语种: |
英语
|
| 中文简介: |
在实际的机器学习问题中, 输入特征向量的一部分不完整是非常常见的: 不可用、缺失或损坏。本文提出了一种将特征与不完全信息和具有完整信息的特征集成起来的提升方法, 形成了一个强大的分类器。通过引入隐藏变量来模拟缺失信息, 我们形成了损失函数, 将完全标记的数据与部分标记的数据结合起来, 有效地学习归一化和非归一化模型。提出的这些损失函数优化问题的主要问题, 以显示它们的密切关系及其背后的动机。我们使用辅助函数来绑定损失函数的变化, 并推导出学习算法的显式参数更新规则。我们在两个现实世界的问题上展示了令人鼓舞的结果--计算机视觉中的视觉对象识别和自然语言处理中的命名实体识别--以显示所提出的提升方法的有效性。
|
| 课程简介: |
In real-world machine learning problems, it is very common that part of the input feature vector is incomplete: either not available, missing, or corrupted. In this paper, we present a boosting approach that integrates features with incomplete information and those with complete information to form a strong classifier. By introducing hidden variables to model missing information, we form loss functions that combine fully labeled data with partially labeled data to effectively learn normalized and unnormalized models. The primal problems of the proposed optimization problems with these loss functions are provided to show their close relationships and the motivations behind them. We use auxiliary functions to bound the change of the loss functions and derive explicit parameter update rules for the learning algorithms. We demonstrate encouraging results on two real-world problems - visual object recognition in computer vision and named entity recognition in natural language processing - to show the effectiveness of the proposed boosting approach.
|
| 关 键 词: |
辅助函数; 损失函数; 计算机视觉和自然语言处理
|
| 课程来源: |
视频讲座网
|
| 最后编审: |
2020-06-03：张荧(课程编辑志愿者)
|
| 阅读次数: |
41
|
|
图 书 馆