优化支持向量机超参数的重采样过程的经验评价
IF 4.3
3区 计算机科学
Q1 AUTOMATION & CONTROL SYSTEMS
引用次数: 0
摘要
调优正则化和核超参数是优化核方法(如支持向量机)泛化性能的关键步骤。这是最常见的……本文章由计算机程序翻译，如有差异，请以英文原文为准。
Empirical evaluation of resampling procedures for optimising SVM hyperparameters
Tuning the regularisation and kernel hyperparameters is a vital step in optimising the generalisation performance of kernel methods, such as the support vector machine (SVM). This is most often per...
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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
2
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JMLR seeks previously unpublished papers on machine learning that contain: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.
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