图 书 馆开课单位--卡内基梅隆大学
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91Constrained Semi-Supervised Learning using Attributes and Comparative Attributes[基于属性和比较属性的约束半监督学习]
Stefan Carlsson, Antonio Torralba, Abhinav Shrivastava(卡内基梅隆大学) We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable a...
热度：80
Stefan Carlsson, Antonio Torralba, Abhinav Shrivastava(卡内基梅隆大学) We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable a...
热度：80
92Bootstrapping Information Extraction from Semi-structured Web Pages[从半结构化网页中提取引导信息]
Charles Schafer, Andrew Carlson(卡内基梅隆大学) We consider the problem of extracting structured records from semi-structured web pages with no human supervision required for each target web site. P...
热度：64
Charles Schafer, Andrew Carlson(卡内基梅隆大学) We consider the problem of extracting structured records from semi-structured web pages with no human supervision required for each target web site. P...
热度：64
93A Joint Topic and Perspective Model for Ideological Discourse[意识形态话语的联合主题与视角模型]
Eric P. Xing, Wei-Hao Lin, Alexander Hauptmann(卡内基梅隆大学) Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideologica...
热度：73
Eric P. Xing, Wei-Hao Lin, Alexander Hauptmann(卡内基梅隆大学) Polarizing discussions on political and social issues are common in mass and user-generated media. However, computer-based understanding of ideologica...
热度：73
94RTG: A Recursive Realistic Graph Generator using Random Typing[RTG:一种生成实景图的随机类型递归模型}
Leman Akoglu(卡内基梅隆大学) We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capabl...
热度：89
Leman Akoglu(卡内基梅隆大学) We propose a new, recursive model to generate realistic graphs, evolving over time. Our model has the following properties: it is (a) flexible, capabl...
热度：89
95The economist as therapist: Behavioural economics and
George Loewenstein(卡内基梅隆大学) We review methodological issues that arise in designing, implementing and evaluating the efficacy of 'light' paternalistic policies. In contra...
热度：92
George Loewenstein(卡内基梅隆大学) We review methodological issues that arise in designing, implementing and evaluating the efficacy of 'light' paternalistic policies. In contra...
热度：92
96On the Chance Accuracies of Large Collections of Classifiers[关于大量量词的机会准确性]
Mark Palatucci(卡内基梅隆大学) We provide a theoretical analysis of the chance accuracies of large collections of classifiers. We show that on problems with small numbers of example...
热度：57
Mark Palatucci(卡内基梅隆大学) We provide a theoretical analysis of the chance accuracies of large collections of classifiers. We show that on problems with small numbers of example...
热度：57
97Regularization and Feature Selection in Least Squares Temporal-Difference Learning[最小二乘时变差分学习中的正则化与特征选择]
J. Zico Kolter(卡内基梅隆大学) We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-S...
热度：110
J. Zico Kolter(卡内基梅隆大学) We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-S...
热度：110
98Efficient high dimensional maximum entropy modeling via symmetric partition functions[通过对称分区函数进行高效的高维最大熵建模]
Paul Vernaza(卡内基梅隆大学) The application of the maximum entropy principle to sequence modeling has been popularized by methods such as Conditional Random Fields (CRFs). Howeve...
热度：55
Paul Vernaza(卡内基梅隆大学) The application of the maximum entropy principle to sequence modeling has been popularized by methods such as Conditional Random Fields (CRFs). Howeve...
热度：55
99Online Learning, Regret Minimization, and Game Theory[在线学习，遗憾最小化和博弈论]
Avrim Blum(卡内基梅隆大学) The first part of tha tutorial will discuss adaptive algorithms for making decisions in uncertain environments (e.g., what route should I take to work...
热度：195
Avrim Blum(卡内基梅隆大学) The first part of tha tutorial will discuss adaptive algorithms for making decisions in uncertain environments (e.g., what route should I take to work...
热度：195
100Learning in Computer Vision[在计算机视觉中学习]
Simon Lucey(卡内基梅隆大学) This tutorial he will cover some of the core fundamentals in vision and demonstrate how they can be interpreted in terms of machine learning fundament...
热度：103
Simon Lucey(卡内基梅隆大学) This tutorial he will cover some of the core fundamentals in vision and demonstrate how they can be interpreted in terms of machine learning fundament...
热度：103