Choice of V for V-fold cross-validation in least-squares density estimation

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:10.5555/2946645.3053490

ArlotSylvain, LerasleMatthieu

引用次数: 0

Abstract

This paper studies V-fold cross-validation for model selection in least-squares density estimation. The goal is to provide theoretical grounds for choosing V in order to minimize the least-squares ...

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最小二乘密度估计中V-fold交叉验证V值的选择

研究了最小二乘密度估计模型选择的v折交叉验证方法。目标是为选择V以最小化最小二乘提供理论依据……

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 JMLR seeks previously unpublished papers on machine learning that contain: new principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or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