汤森路透每年出版一本《期刊引用报告》(Journal Citation Reports,简称JCR)。JCR对86000多种SCI期刊的影响因子(Impact Factor)等指数加以统计。JCR将收录期刊分为176个不同学科类别在JCR的Journal Ranking中,主要参考当年IF,最终每个分区的期刊数量是均分的。
Media Ranking in United States Subject Area and Category Computer Science Artificial Intelligence Software Engineering Control and Systems Engineering Mathematics Statistics and Probability Publisher Microtome Publishing H-Index 261 Publication type Journals ISSN 15324435, 15337928 Coverage 2001-2022 Information...
IEEE Transactions on Neural Networks and Learning Systems (was IEEE Transactions on Neural Networks) A* IEEE Transactions on Parallel and Distributed Systems A* IEEE Transactions on Pattern Analysis and Machine Intelligence A* IEEE Transactions on Robotics A* IEEE Transactions on Services Computing A* ...
26International Journal of Machine Learning and Cyberneticsjournal0.988Q1653216481543030596434.0548.0733.39 27Intelligent Systems with Applicationsjournal0.959Q11813911374137441135.8153.3323.06 28International Journal of Interactive Multimedia and Artificial Intelligencejournal0.904Q2236319426547061803.3742.1316.10 ...
汤森路透每年出版一本《期刊引用报告》(Journal Citation Reports,简称JCR)。JCR对86000多种SCI期刊的影响因子(Impact Factor)等指数加以统计。JCR将收录期刊分为176个不同学科类别在JCR的Journal Ranking中,主要参考当年IF,最终每个分区的期刊数量是均分的。
Journal of Machine Learning Research () Submitted; Published Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary β-Mixing Processes Pac-Bayes bounds are among the most accurate generalization bounds for classifiers learned from independently and identically distributed (IID...
Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling[J].The Journal of Machine Learning,2000,(08)... W Stefan,S Tobias 被引量: 0发表: 2002年 The Knowledge-Gradient Policy for Correlated Normal Beliefs We consider a Bayesian ranking and selection problem with...
Chromatic PAC-Bayes Bounds for Non-IID Data: Applications to Ranking and Stationary $beta$-Mixing Processes Journal of Machine Learning Research, 11, 1927-1956.Chromatic PAC-Bayes bounds for non-IID data: Applications to ranking and stationary β-mixing processes. Ralaivola, Liva,Szafranski, Mari...
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences arises in several applications, such as that of combining the results of different search engines, or the "collaborativefilte...
We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produ...