雜志介紹

Journal Of Machine Learning Research雜志介紹

《Journal Of Machine Learning Research》是一本以English為主的開放獲取國際優(yōu)秀期刊，中文名稱機(jī)器學(xué)習(xí)研究雜志，本刊主要出版、報道計算機(jī)科學(xué)-AUTOMATION & CONTROL SYSTEMS領(lǐng)域的研究動態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國際權(quán)威數(shù)據(jù)庫SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為4.3，最新CiteScore 指數(shù)為18.8。

本刊近期中國學(xué)者發(fā)表的論文主要有：

• Differentially private data releasing for smooth queries

  Author: wangzt2012

• SPSD matrix approximation vis column selection: theories, algorithms, and extensions

  Author: zhihua

• SPSD matrix approximation vis column selection: theories, algorithms, and extensions

  Author: ricky

• Hybrid orthogonal projection and estimation (HOPE): a new framework to learn neural networks

  Author: zsl2008

英文介紹

Journal Of Machine Learning Research雜志英文介紹

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JMLR seeks previously unpublished papers on machine learning that contain: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.