{"title":"Fast cross-validation via sequential testing","authors":"KruegerTammo, PankninDanny, BraunMikio","doi":"10.5555/2789272.2886786","DOIUrl":null,"url":null,"abstract":"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...","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"1 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2015-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Machine Learning Research","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.5555/2789272.2886786","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 2
Abstract
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...
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JMLR seeks previously unpublished papers on machine learning that contain: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.
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