图 书 馆开课单位--密歇根大学
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41Large-Scale Sparse Logistic Regression [大型稀疏Logistic回归]
Jieping Ye(密歇根大学) Logistic回归方法，已被广泛应用于数据挖掘，机器学习，计算机视觉的许多应用中一个著名的分类方法，生物信息学。稀疏Logistic回归嵌入特征选择使用L1范数正则化...
热度：736
Jieping Ye(密歇根大学) Logistic回归方法，已被广泛应用于数据挖掘，机器学习，计算机视觉的许多应用中一个著名的分类方法，生物信息学。稀疏Logistic回归嵌入特征选择使用L1范数正则化...
热度：736
42The NSF Cyberinfrastructure Initiative: Vision and Implementation Towards Learning and Discovery without Barriers[NSF网络基础设施倡议：愿景和实现学习和发现的无障碍]
Daniel Atkins(密歇根大学) Quite recently, the National Science Foundation rewarded Daniel Atkins for his unique contributions to high-performance computing and distributed know...
热度：55
Daniel Atkins(密歇根大学) Quite recently, the National Science Foundation rewarded Daniel Atkins for his unique contributions to high-performance computing and distributed know...
热度：55
43Mining Discrete Patterns via Binary Matrix Factorization[通过二元矩阵分解挖掘离散模式]
Jieping Ye(密歇根大学) Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rank-one binary matrix approximations t...
热度：58
Jieping Ye(密歇根大学) Mining discrete patterns in binary data is important for subsampling, compression, and clustering. We consider rank-one binary matrix approximations t...
热度：58
44Nonlinear Adaptive Distance Metric Learning for Clustering [用于聚类的非线性自适应距离度量学习]
Jieping Ye(密歇根大学) A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project o...
热度：241
Jieping Ye(密歇根大学) A good distance metric is crucial for many data mining tasks. To learn a metric in the unsupervised setting, most metric learning algorithms project o...
热度：241
45Measuring Health Disparities[测量健康差异]
pkleymee(密歇根大学) Content Type: Course Measuring Health Disparities is designed to be accessible to a broad audience of practitioners across all sectors of the pu...
热度：200
pkleymee(密歇根大学) Content Type: Course Measuring Health Disparities is designed to be accessible to a broad audience of practitioners across all sectors of the pu...
热度：200
46Statistics 250 - Introduction to Statistics and Data Analysis[统计 250-统计和数据分析导论]
Brenda Gunderson(密歇根大学) This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of loca...
热度：198
Brenda Gunderson(密歇根大学) This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of loca...
热度：198
47Beyond DCG: User Behavior as a Predictor of a Successful Search[除了动态心电图：作为一个成功的搜索用户行为预测]
Ahmed Hassan(密歇根大学) Web search engines are traditionally evaluated in terms of the relevance of web pages to individual queries. However, relevance of web pages does not ...
热度：45
Ahmed Hassan(密歇根大学) Web search engines are traditionally evaluated in terms of the relevance of web pages to individual queries. However, relevance of web pages does not ...
热度：45
48Privacy in Interaction: Exploring Disclosure and Social Capital in Facebook[互动中的隐私:探索脸书网的信息披露与社会资本]
Ellison Nicole B(密歇根大学) In this paper, we explore the relationship between Facebook users’ privacy concerns, relationship maintenance strategies, and social capital out...
热度：50
Ellison Nicole B(密歇根大学) In this paper, we explore the relationship between Facebook users’ privacy concerns, relationship maintenance strategies, and social capital out...
热度：50
4950Projection onto A Nonnegative Max-Heap[在非负最大堆上的投影]
Jieping Ye(密歇根大学) We consider the problem of computing the Euclidean projection of a vector of length $p$ onto a non-negative max-heap - an ordered tree where the value...
热度：51
Jieping Ye(密歇根大学) We consider the problem of computing the Euclidean projection of a vector of length $p$ onto a non-negative max-heap - an ordered tree where the value...
热度：51