Knowledge Commons of Institute of Automation,CAS
Information Theory and Its Relation to Machine Learning | |
Hu, Bao-Gang![]() | |
2015 | |
会议名称 | Chinese Intelligent Automation Conference |
会议日期 | 2015 |
会议地点 | Fuzhou, China |
摘要 |
In this position paper, I first describe a new perspective on machine learning (ML) by four basic problems (or levels), namely “What to learn?”, “How to learn?”, “What to evaluate?”, and “What to adjust?”. The paper stresses more on the first level of “What to learn?”, or “Learning Target Selection”. Toward this primary problem within the four levels, I briefly review the existing studies about the connection between information theoretical learning (ITL [1]) and machine
learning. A theorem is given on the relation between the empirically-defined similarity measure and information measures. Finally, a conjecture is proposed for pursuing a unified mathematical interpretation to learning target selection. |
关键词 | Machine Learning Learning Target Selection Entropy Information Theory Similarity Conjecture |
DOI | 10.1007/978-3-662-46469-4_1 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20008 |
专题 | 多模态人工智能系统全国重点实验室_多媒体计算 |
通讯作者 | Hu, Bao-Gang |
作者单位 | National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
第一作者单位 | 模式识别国家重点实验室 |
通讯作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Hu, Bao-Gang. Information Theory and Its Relation to Machine Learning[C],2015. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Information Theory a(347KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
个性服务 |
推荐该条目 |
保存到收藏夹 |
查看访问统计 |
导出为Endnote文件 |
谷歌学术 |
谷歌学术中相似的文章 |
[Hu, Bao-Gang]的文章 |
百度学术 |
百度学术中相似的文章 |
[Hu, Bao-Gang]的文章 |
必应学术 |
必应学术中相似的文章 |
[Hu, Bao-Gang]的文章 |
相关权益政策 |
暂无数据 |
收藏/分享 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论