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 JMLR seeks previously unpublished papers on machine learning that contain:New principled algorithms with sound empirical validation, and with justification of theoretical, psychological, or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;Extremely well-written surveys of existing work.

《機(jī)器學(xué)習(xí)研究雜志》（JMLR）提供了一個(gè)國際論壇，用于電子和紙質(zhì)出版機(jī)器學(xué)習(xí)各個(gè)領(lǐng)域的高質(zhì)量學(xué)術(shù)文章。所有發(fā)表的論文均可在網(wǎng)上免費(fèi)查閱。JMLR尋求以前未發(fā)表的關(guān)于機(jī)器學(xué)習(xí)的論文，其中包含：新的原則性算法，具有良好的經(jīng)驗(yàn)驗(yàn)證，并具有理論、心理或生物學(xué)性質(zhì)的合理性；實(shí)驗(yàn)或理論研究，對智能系統(tǒng)中的學(xué)習(xí)設(shè)計(jì)和行為產(chǎn)生新的見解；說明現(xiàn)有技術(shù)的應(yīng)用，闡明這些方法的優(yōu)缺點(diǎn)；正式化新的學(xué)習(xí)任務(wù)（例如，在新的應(yīng)用環(huán)境中）和評估這些任務(wù)績效的方法；開發(fā)新的分析框架，促進(jìn)實(shí)踐學(xué)習(xí)方法的理論研究；自然學(xué)習(xí)系統(tǒng)在行為或神經(jīng)層面上的數(shù)據(jù)計(jì)算模型；對現(xiàn)有工作的非常好的書面調(diào)查。