图 书 馆开课单位--澳大利亚国立大学
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1CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison[CoCoOn：用于IaaS价格和性能比较的云计算本体]
Armin Haller(澳大利亚国立大学) CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison
热度：51
Armin Haller(澳大利亚国立大学) CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison
热度：51
2Machine Learning in Bioinformatics[生物信息学中的机器学习]
Alexander Zien(澳大利亚国立大学) Machine Learning in Bioinformatics
热度：26
Alexander Zien(澳大利亚国立大学) Machine Learning in Bioinformatics
热度：26
3Relations Betweeen Machine Learning Problems[机器学习问题之间的关系]
Robert C.Williamson(澳大利亚国立大学) Relations Betweeen Machine Learning Problems
热度：26
Robert C.Williamson(澳大利亚国立大学) Relations Betweeen Machine Learning Problems
热度：26
4The Semantic Sensor Network Ontology, Revamped[语义传感器网络本体，改进]
Kerry Taylor(澳大利亚国立大学) The Semantic Sensor Network Ontology, Revamped
热度：25
Kerry Taylor(澳大利亚国立大学) The Semantic Sensor Network Ontology, Revamped
热度：25
5dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees[dK微聚合：具有差异隐私保证的匿名图]
Masooma Iftikhar(澳大利亚国立大学) dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees
热度：28
Masooma Iftikhar(澳大利亚国立大学) dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees
热度：28
6Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks[基于双超图卷积网络的多重二部网络嵌入]
Hansheng Xue(澳大利亚国立大学) Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
热度：85
Hansheng Xue(澳大利亚国立大学) Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks
热度：85
7Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series[Radflow：时间序列网络的循环、聚合和可分解模型]
Alasdair Tran(澳大利亚国立大学) Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
热度：35
Alasdair Tran(澳大利亚国立大学) Radflow: A Recurrent, Aggregated, and Decomposable Model for Networks of Time Series
热度：35
8Learning without Concentration[不专心学习]
Shahar Mendelson(澳大利亚国立大学) We obtain sharp bounds on the convergence rate of Empirical Risk Minimization performed in a convex class and with respect to the squared loss, withou...
热度：43
Shahar Mendelson(澳大利亚国立大学) We obtain sharp bounds on the convergence rate of Empirical Risk Minimization performed in a convex class and with respect to the squared loss, withou...
热度：43
9Intelligent Agents[智能代理]
John Lloyd(澳大利亚国立大学) An agent is an entity that receives percepts from the environment in which it is operating and applies actions to the environment in order to achieve ...
热度：53
John Lloyd(澳大利亚国立大学) An agent is an entity that receives percepts from the environment in which it is operating and applies actions to the environment in order to achieve ...
热度：53
10Sparse Kernel-SARSA(λ) with an Eligibility Trace[稀疏核Sarsa（λ）一个合格的痕迹]
Matthew Robards(澳大利亚国立大学) We introduce the first online kernelized version of SARSA(λ) to permit sparsification for arbitrary λ for 0 ≤ λ ≤ 1; this i...
热度：497
Matthew Robards(澳大利亚国立大学) We introduce the first online kernelized version of SARSA(λ) to permit sparsification for arbitrary λ for 0 ≤ λ ≤ 1; this i...
热度：497