图 书 馆开课单位--阿尔伯塔大学
21
22
23
24
25
26
27
28
29
30
21Improved Algorithms for Linear Stochastic Bandits[线性随机带的改进算法]
Yasin Abbasi-Yadkori(阿尔伯塔大学) We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic mu...
热度：104
Yasin Abbasi-Yadkori(阿尔伯塔大学) We improve the theoretical analysis and empirical performance of algorithms for the stochastic multi-armed bandit problem and the linear stochastic mu...
热度：104
22Introduction to Reinforcement Learning[强化学习简介]
Csaba Szepesvári(阿尔伯塔大学) The tutorial will introduce Reinforcement Learning, that is, learning what actions to take, and when to take them, so as to optimize long-term per...
热度：92
Csaba Szepesvári(阿尔伯塔大学) The tutorial will introduce Reinforcement Learning, that is, learning what actions to take, and when to take them, so as to optimize long-term per...
热度：92
23Efficient Discriminative Training Method for Structured Predictions[结构预测的有效判别训练方法]
Huizhen Yu(阿尔伯塔大学) We propose an efficient discriminative training method for generative models under supervised learning. In our setting, fully observed instances are g...
热度：53
Huizhen Yu(阿尔伯塔大学) We propose an efficient discriminative training method for generative models under supervised learning. In our setting, fully observed instances are g...
热度：53
24RL Glue and Codecs Glue[RL胶水和编解码胶水]
Brian Tanner(阿尔伯塔大学) RL-Glue is a protocol and software implementation for evaluating reinforcement learning algorithms. Our system facilitates the comparison of alternati...
热度：33
Brian Tanner(阿尔伯塔大学) RL-Glue is a protocol and software implementation for evaluating reinforcement learning algorithms. Our system facilitates the comparison of alternati...
热度：33
25Agnostic KWIK learning and efficient approximate reinforcement learning[不可知的KWIK学习和有效的近似强化学习]
Csaba Szepesvári(阿尔伯塔大学) A popular approach in reinforcement learning is to use a model-based algorithm, i.e., an algorithm that utilizes a model learner to learn an approxima...
热度：55
Csaba Szepesvári(阿尔伯塔大学) A popular approach in reinforcement learning is to use a model-based algorithm, i.e., an algorithm that utilizes a model learner to learn an approxima...
热度：55
26SIGKDD Explorations Newsletter Report[Sigkdd探索新闻稿报告]
Osmar R. Zaïane(阿尔伯塔大学) Explorations is published twice yearly, in June/July and in December/January each year. The newsletter is distributed in hardcopy form to all members ...
热度：212
Osmar R. Zaïane(阿尔伯塔大学) Explorations is published twice yearly, in June/July and in December/January each year. The newsletter is distributed in hardcopy form to all members ...
热度：212
27Identfying Potentiallcy Important Conepts and Relations in an Ontology[本体论中重要概念与关系的识别 ]
Gang Wu(阿尔伯塔大学 ) More and more ontologies have been published and used widely on the web. In order to make good use of an ontology, especially a new and complex ontolo...
热度：35
Gang Wu(阿尔伯塔大学 ) More and more ontologies have been published and used widely on the web. In order to make good use of an ontology, especially a new and complex ontolo...
热度：35
28Extracting Meta Statements from the Blogosphere[从博客圈中提取元语句]
Filipe Mesquita(阿尔伯塔大学) Information extraction systems have been recently proposed for organizing and exploring content in large online text corpora as information networks. ...
热度：26
Filipe Mesquita(阿尔伯塔大学) Information extraction systems have been recently proposed for organizing and exploring content in large online text corpora as information networks. ...
热度：26
29Optimal Reverse Prediction: A Unified Perspective on Supervised, Unsupervised and Semi-Supervised Learning[最优逆向预测：监督，无监督和半监督学习的统一视角]
Linli Xu(阿尔伯塔大学) raining principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning, causing a prolif...
热度：32
Linli Xu(阿尔伯塔大学) raining principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning, causing a prolif...
热度：32
30Fast Gradient-Descent Methods for Temporal-Difference Learning with Linear Function Approximation[线性函数逼近的时差学习快速梯度下降法]
Richard S. Sutton(阿尔伯塔大学) Sutton, Szepesvari and Maei (2009) recently introduced the first temporal-difference learning algorithm compatible with both linear function approxima...
热度：83
Richard S. Sutton(阿尔伯塔大学) Sutton, Szepesvari and Maei (2009) recently introduced the first temporal-difference learning algorithm compatible with both linear function approxima...
热度：83