Dimension-free concentration bounds on hankel matrices for spectral learning

IF 4.3 3区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Journal of Machine Learning Research Pub Date : 2016-01-01 DOI:10.5555/2946645.2946676

DenisFrançois, GybelsMattias, HabrardAmaury

引用次数: 1

Abstract

Learning probabilistic models over strings is an important issue for many applications. Spectral methods propose elegant solutions to the problem of inferring weighted automata from finite samples ...

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用于谱学习的汉克尔矩阵的无维集中界

学习字符串的概率模型对于许多应用来说是一个重要的问题。谱方法为从有限样本中推断加权自动机的问题提供了优雅的解决方案。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Machine Learning Research 工程技术-计算机：人工智能

CiteScore

18.80

自引率

0.00%

发文量

审稿时长

3 months

期刊介绍： The Journal of Machine Learning Research (JMLR) provides an international forum for the electronic and paper publication of high-quality scholarly articles in all areas of machine learning. All published papers are freely available online. JMLR has a commitment to rigorous yet rapid reviewing. JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or biological nature; experimental and/or theoretical studies yielding new insight into the design and behavior of learning in intelligent systems; accounts of applications of existing techniques that shed light on the strengths and weaknesses of the methods; formalization of new learning tasks (e.g., in the context of new applications) and of methods for assessing performance on those tasks; development of new analytical frameworks that advance theoretical studies of practical learning methods; computational models of data from natural learning systems at the behavioral or neural level; or extremely well-written surveys of existing work.