图 书 馆开课单位--俄勒冈州立大学
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1112Iterative Learning of Weighted Rule Sets for Greedy Search[贪婪搜索的加权规则集迭代学习]
Yuehua Xu(俄勒冈州立大学) Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider le...
热度：30
Yuehua Xu(俄勒冈州立大学) Greedy search is commonly used in an attempt to generate solutions quickly at the expense of completeness and optimality. In this work, we consider le...
热度：30
13Automatic Discovery and Transfer of MAXQ Hierarchies[自动发现和MAXQ层次转移 ]
Neville Mehta(俄勒冈州立大学) We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian n...
热度：38
Neville Mehta(俄勒冈州立大学) We present an algorithm, HI-MAT (Hierarchy Induction via Models And Trajectories), that discovers MAXQ task hierarchies by applying dynamic Bayesian n...
热度：38
14Fast and Accurate k-means For Large Datasets[大数据集快速准确的k均值]
Michael Shindler(俄勒冈州立大学) Clustering is a popular problem with many applications. We consider the $k$-means problem in the situation where the data is too large to be stored in...
热度：68
Michael Shindler(俄勒冈州立大学) Clustering is a popular problem with many applications. We consider the $k$-means problem in the situation where the data is too large to be stored in...
热度：68
15Budgeted Optimization with Concurrent Stochastic-Duration Experiments[并行随机时长实验的预算优化]
Javad Azimi(俄勒冈州立大学) Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited number of function evaluations at int...
热度：35
Javad Azimi(俄勒冈州立大学) Budgeted optimization involves optimizing an unknown function that is costly to evaluate by requesting a limited number of function evaluations at int...
热度：35
16Category Detection Using Hierarchical Mean Shift[基于层次均值漂移的分类检测]
Weng-Keen Wong(俄勒冈州立大学) Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods ...
热度：25
Weng-Keen Wong(俄勒冈州立大学) Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods ...
热度：25
17Local Decomposition for Rare Class Analysis [稀有类分析的局部分解 ]
Junjie Wu(俄勒冈州立大学 ) Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. ...
热度：50
Junjie Wu(俄勒冈州立大学 ) Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. ...
热度：50
18Learning for Efficient Retrieval of Structured Data with Noisy Queries[利用噪声查询学习有效检索结构化数据]
Charles Parker(俄勒冈州立大学) Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider ...
热度：52
Charles Parker(俄勒冈州立大学) Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider ...
热度：52
19Ensemble Monte-Carlo Planning: An Empirical Study[合奏蒙特卡罗计划:一个实证研究 ]
Alan Fern(俄勒冈州立大学) Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the availabl...
热度：52
Alan Fern(俄勒冈州立大学) Monte-Carlo planning algorithms, such as UCT, select actions at each decision epoch by intelligently expanding a single search tree given the availabl...
热度：52
20Monte-Carlo Planning: Basic Principles and Recent Progress[蒙特卡洛规划：基本原则和最新进展]
Alan Fern(俄勒冈州立大学) Many planning applications are difficult to model in standard domain description languages. However, with out the limitations of a particular language...
热度：132
Alan Fern(俄勒冈州立大学) Many planning applications are difficult to model in standard domain description languages. However, with out the limitations of a particular language...
热度：132