而现在Lafferty做的东西好像很杂,semi-supervised learning, kernel learning,graphical models甚至manifold learning都有涉及,可能就是像武侠里一样只要学会了九阳神功,那么其它的武功就可以一窥而知其精髓了。这里面我最喜欢的是semi-supervised learning,因为随着要处理的数据越来越多,进行全部label过于困难,而完全unsuperv...
虽然江湖传说计算机重镇CMU现在在走向衰落,但这无碍Lafferty拥有越来越大的影响力,翻开AI兵器谱排名第一的journal of machine learning research的很多文章,我们都能发现author或者editor中赫然有Lafferty的名字。 Lafferty给人留下的最大的印象似乎是他2001年的conditional random fields,这篇文章后来被疯狂引用,广泛地应用...
1 Machine LearningNLD67%similarity2 Information and InferenceUSA60%similarity3 Transactions of the Japanese Society for Artificial IntelligenceJPN52%similarity4 Transactions of the Association for Computational LinguisticsUSA49%similarity5 Statistics and ComputingNLD47%similarity6 Journal of Computational and Gra...
Learning-to-rank is one of the learning frameworks in machine learning and it aims to organize the objects in a particular order according to their preference, relevance or ranking. In this paper, we give a comprehensive survey for learning-to-rank. First, we discuss the different approaches ...
机器学习研究杂志(Journal Of Machine Learning Research)是一本由Microtome Publishing出版的一本工程技术-计算机:人工智能学术刊物,主要报道工程技术-计算机:人工智能相关领域研究成果与实践。本刊已入选来源期刊,该刊创刊于2001年,出版周期Bimonthly。2021-2022年最新版WOS分区等级:Q1,2023年发布的影响因子为4.3,CiteScor...
国际机器学习与控制论杂志(International Journal Of Machine Learning And Cybernetics)是一本由Springer Berlin Heidelberg出版的一本COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE学术刊物,主要报道COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE相关领域研究成果与实践。本刊已入选来源期刊,该刊创刊于2010年,出版周期12 issues per ye...
- 《Journal of Machine Learning Research》 被引量: 30发表: 2014年 An automated ranking platform for machine learning regression models for meat spoilage prediction using multi-spectral imaging and metabolic profiling MeatReg" was tested with minced beef samples stored under aerobic and modified ...
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...
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...