通过顺序测试快速交叉验证

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

Journal of Machine Learning Research Pub Date : 2015-01-01 DOI:10.5555/2789272.2886786

KruegerTammo, PankninDanny, BraunMikio

引用次数: 2

摘要

随着当今数据集的规模越来越大，通过交叉验证在模型选择中找到正确的参数配置可能是一项非常耗时的任务。在本文中，我们提出了一个…

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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Fast cross-validation via sequential testing

With the increasing size of today's data sets, nding the right parameter configuration in model selection via cross-validation can be an extremely time-consuming task. In this paper we propose an i...

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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 JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or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.