图 书 馆开课单位--卡内基梅隆大学
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181The “FAME” Interactive Space[“FAME”互动空间]
Florian Metze(卡内基梅隆大学) This paper describes the “FAME” multi-modal demonstrator, which integrates multiple communication modes – vision, speech and object ...
热度：69
Florian Metze(卡内基梅隆大学) This paper describes the “FAME” multi-modal demonstrator, which integrates multiple communication modes – vision, speech and object ...
热度：69
182Fast Query Execution for Retrieval Models Based on Path-Constrained Random Walks[基于路径约束随机游动的检索模型快速查询执行 ]
Ni Lao(卡内基梅隆大学) Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, ...
热度：76
Ni Lao(卡内基梅隆大学) Many recommendation and retrieval tasks can be represented as proximity queries on a labeled directed graph, with typed nodes representing documents, ...
热度：76
183Data Mining to Predict and Prevent Errors in Health Insurance Claims Processing[用于预测和预防医疗保险索赔处理错误的数据挖掘]
Mohit Kumar(卡内基梅隆大学 ) Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insur...
热度：70
Mohit Kumar(卡内基梅隆大学 ) Health insurance costs across the world have increased alarmingly in recent years. A major cause of this increase are payment errors made by the insur...
热度：70
184Discriminative Topic Modeling based on Manifold Learning[基于流形学习的主题识别模型 ]
Seungil Huh(卡内基梅隆大学) Topic modeling has been popularly used for data analysis in various domains including text documents. Previous topic models, such as probabilistic Lat...
热度：96
Seungil Huh(卡内基梅隆大学) Topic modeling has been popularly used for data analysis in various domains including text documents. Previous topic models, such as probabilistic Lat...
热度：96
185Indexing and Mining Time Sequences[索引和挖掘时间序列 ]
Christos Faloutsos, Lei Li(卡内基梅隆大学 ) How can we find patterns in a sequence of sensor measurements (eg., a sequence of temperatures, or water-pollutant measurements)? How can we compress ...
热度：47
Christos Faloutsos, Lei Li(卡内基梅隆大学 ) How can we find patterns in a sequence of sensor measurements (eg., a sequence of temperatures, or water-pollutant measurements)? How can we compress ...
热度：47
186Mining Social Networks for Personalized Email Prioritizationp[挖掘社交网络以实现个性化电子邮件优先级]
Shinjae Yoo(卡内基梅隆大学) Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate ema...
热度：40
Shinjae Yoo(卡内基梅隆大学) Email is one of the most prevalent communication tools today, and solving the email overload problem is pressingly urgent. A good way to alleviate ema...
热度：40
187TANGENT: A Novel, 'Surprise-me' Recommendation Algorithm[TANGENT：一种新颖的“惊喜我”推荐算法 ]
Kensuke Onuma(卡内基梅隆大学) Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me"...
热度：46
Kensuke Onuma(卡内基梅隆大学) Most of recommender systems try to find items that are most relevant to the older choices of a given user. Here we focus on the "surprise me"...
热度：46
188Large Graph-Mining: Power Tools and a Practitioner's Guide[大型图形挖掘：电动工具和实践指南]
Charalampos E. Tsourakakis, Christos Faloutsos, Gary L Miller(卡内基梅隆大学) Numerous real-world datasets are in matrix form, thus matrix algebra, linear and multilinear, provides important algorithmic tools for analyzing them....
热度：50
Charalampos E. Tsourakakis, Christos Faloutsos, Gary L Miller(卡内基梅隆大学) Numerous real-world datasets are in matrix form, thus matrix algebra, linear and multilinear, provides important algorithmic tools for analyzing them....
热度：50
189Statistical Challenges in Computational Advertising[计算广告中的统计挑战]
Deepayan Chakrabarti, Deepak Agarwal(卡内基梅隆大学) Many organizations now devote significant fractions of their advertising/outreach budgets to online advertising; ad-networks like Yahoo!, Google, MSN ...
热度：80
Deepayan Chakrabarti, Deepak Agarwal(卡内基梅隆大学) Many organizations now devote significant fractions of their advertising/outreach budgets to online advertising; ad-networks like Yahoo!, Google, MSN ...
热度：80
190Detecting Anomalous Records in Categorical Datasets [分类数据集中异常记录的检测]
Kaustav Das(卡内基梅隆大学 ) We consider the problem of detecting anomalies in high arity categorical datasets. In most applications, anomalies are defined as data points that are...
热度：79
Kaustav Das(卡内基梅隆大学 ) We consider the problem of detecting anomalies in high arity categorical datasets. In most applications, anomalies are defined as data points that are...
热度：79