图 书 馆开课单位--墨尔本大学
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1A Framework for Feature Selection to Exploit Feature Group Structures[利用特征组结构的特征选择框架]
Kushani Perera(墨尔本大学) A Framework for Feature Selection to Exploit Feature Group Structures
热度：21
Kushani Perera(墨尔本大学) A Framework for Feature Selection to Exploit Feature Group Structures
热度：21
2Group based Unsupervised Feature Selection[基于组的无监督特征选择]
Kushani Perera(墨尔本大学) Group based Unsupervised Feature Selection
热度：25
Kushani Perera(墨尔本大学) Group based Unsupervised Feature Selection
热度：25
3Einstein's revolution: Quantum technology for the 21st century quantum computer[爱因斯坦的革命：21世纪量子计算机的量子技术]
David Jamieson(墨尔本大学) instein’s most revolutionary idea, of the light quantum, has led to the concept for a radical new type of computer that uses the strange rules o...
热度：46
David Jamieson(墨尔本大学) instein’s most revolutionary idea, of the light quantum, has led to the concept for a radical new type of computer that uses the strange rules o...
热度：46
4A Hierarchical Information Theoretic Technique for the Discovery of Non Linear Alternative Clusterings[一种非线性替代聚类发现层次信息理论技术]
James Bailey(墨尔本大学) Discovery of alternative clusterings is an important method for exploring complex datasets. It provides the capability for the user to view clustering...
热度：60
James Bailey(墨尔本大学) Discovery of alternative clusterings is an important method for exploring complex datasets. It provides the capability for the user to view clustering...
热度：60
5Data Mining[数据的挖掘]
Rao Kotagiri(墨尔本大学) The ability to distinguish, differentiate and contrast between different datasets is a key objective in data mining. Such an ability can assist domain...
热度：51
Rao Kotagiri(墨尔本大学) The ability to distinguish, differentiate and contrast between different datasets is a key objective in data mining. Such an ability can assist domain...
热度：51
6Contrast Data Mining: Methods and Applications[对比数据挖掘：方法和应用]
Rao Kotagiri(墨尔本大学) The ability to distinguish, differentiate and contrast between different datasets is a key objective in data mining. Such an ability can assist doma...
热度：62
Rao Kotagiri(墨尔本大学) The ability to distinguish, differentiate and contrast between different datasets is a key objective in data mining. Such an ability can assist doma...
热度：62
7Spectral Clustering with Inconsistent Advice[不一致意见的光谱聚类]
Tom Coleman(墨尔本大学) Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorpo...
热度：30
Tom Coleman(墨尔本大学) Clustering with advice (often known as constrained clustering) has been a recent focus of the data mining community. Success has been achieved incorpo...
热度：30
8Fast approximate text document clustering using Compressive Sampling[使用压缩采样后速度近似文本文档聚类 ]
Laurence A. F. Park(墨尔本大学) Document clustering involves repetitive scanning of a document set, therefore as the size of the set increases, the time required for the clustering t...
热度：25
Laurence A. F. Park(墨尔本大学) Document clustering involves repetitive scanning of a document set, therefore as the size of the set increases, the time required for the clustering t...
热度：25
9The Sensitivity of Latent Dirichlet Allocation for Information Retrieval[信息检索中潜在dirichlet分配的敏感性]
Laurence A. F. Park(墨尔本大学) It has been shown that the use of topic models for Information retrieval provides an increase in precision when used in the appropriate form. Latent D...
热度：56
Laurence A. F. Park(墨尔本大学) It has been shown that the use of topic models for Information retrieval provides an increase in precision when used in the appropriate form. Latent D...
热度：56
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