图 书 馆开课单位--德克萨斯大学
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61Online 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：60
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：60
62Hierarchical 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：76
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：76
63Online 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：35
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：35
64Got 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：52
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：52
65A 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：136
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：136
66Learning 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：61
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：61
67Single 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：61
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：61
68Graphical 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：41
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：41
69Robust 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：57
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：57