开课单位--德克萨斯大学
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Online Structure Learning for Markov Logic Networks[马尔可夫逻辑网络的在线结构学习]
Raymond J. Mooney(德克萨斯大学) Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible ...
热度:62
Raymond J. Mooney(德克萨斯大学) Most existing learning methods for Markov Logic Networks (MLNs) use batch training, which becomes computationally expensive and eventually infeasible ...
热度:62
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Hierarchical Model-Based Reinforcement Learning[基于层次模型的强化学习]
Nicholas Jong(德克萨斯大学) Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying st...
热度:77
Nicholas Jong(德克萨斯大学) Hierarchical decomposition promises to help scale reinforcement learning algorithms naturally to real-world problems by exploiting their underlying st...
热度:77
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Online Kernel Selection for Bayesian Reinforcement Learning[贝叶斯强化学习的在线核选择]
Joseph Reisinger(德克萨斯大学) Kernel-based Bayesian methods for Reinforcement Learning (RL) such as Gaussian Process Temporal Difference (GPTD) are particularly promising because t...
热度:36
Joseph Reisinger(德克萨斯大学) Kernel-based Bayesian methods for Reinforcement Learning (RL) such as Gaussian Process Temporal Difference (GPTD) are particularly promising because t...
热度:36
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Got Facebook? Investigating What's Social About Social Media[有脸书网吗?调查社交媒体的社会性]
S Craig Watkins(德克萨斯大学) Watkins has been researching young people’s media behaviors for more than ten years and he teaches in the departments of Radio - Television - Fi...
热度:55
S Craig Watkins(德克萨斯大学) Watkins has been researching young people’s media behaviors for more than ten years and he teaches in the departments of Radio - Television - Fi...
热度:55
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A Self-Training Approach to Cost Sensitive Uncertainty Sampling[成本敏感不确定性抽样的自训练方法]
Joydeep Ghosh(德克萨斯大学) Uncertainty sampling is an effective method for performing active learning that is computationally efficient compared to other active learning methods...
热度:137
Joydeep Ghosh(德克萨斯大学) Uncertainty sampling is an effective method for performing active learning that is computationally efficient compared to other active learning methods...
热度:137
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Learning language from its perceptual context[从感性的角度学习语言]
Raymond J. Mooney(德克萨斯大学) Current systems that learn to process natural language require laboriously constructed human-annotated training data. Ideally, a computer would be abl...
热度:62
Raymond J. Mooney(德克萨斯大学) Current systems that learn to process natural language require laboriously constructed human-annotated training data. Ideally, a computer would be abl...
热度:62
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Single and Multiple Index Models[单一和多个指数模型]
Pradeep Ravikumar(德克萨斯大学) Statistical estimation in the high-dimensional setting, with more variables than samples, has been the focus of considerable research over the last de...
热度:68
Pradeep Ravikumar(德克萨斯大学) Statistical estimation in the high-dimensional setting, with more variables than samples, has been the focus of considerable research over the last de...
热度:68
![](functions/showpic.php?filename=2016060304015956.png)
Graphical Models via Generalized Linear Models[基于广义线性模型的图形化模型]
Eunho Yang(德克萨斯大学) Undirected graphical models, or Markov networks, such as Gaussian graphical models and Ising models enjoy popularity in a variety of applications. In ...
热度:46
Eunho Yang(德克萨斯大学) Undirected graphical models, or Markov networks, such as Gaussian graphical models and Ising models enjoy popularity in a variety of applications. In ...
热度:46
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Robust PCA and Collaborative Filtering: Rejecting Outliers, Identifying Manipulators[鲁棒主成分分析和协同过滤:剔除异常值,识别操纵器]
Constantine Caramanis(德克萨斯大学) Principal Component Analysis is one of the most widely used techniques for dimensionality reduction. Nevertheless, it is plagued by sensitivity to out...
热度:59
Constantine Caramanis(德克萨斯大学) Principal Component Analysis is one of the most widely used techniques for dimensionality reduction. Nevertheless, it is plagued by sensitivity to out...
热度:59