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» Learning from sensor network data
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137
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KDD
2008
ACM
259views Data Mining» more  KDD 2008»
16 years 3 months ago
Using ghost edges for classification in sparsely labeled networks
We address the problem of classification in partially labeled networks (a.k.a. within-network classification) where observed class labels are sparse. Techniques for statistical re...
Brian Gallagher, Hanghang Tong, Tina Eliassi-Rad, ...
145
Voted
ISCIS
2005
Springer
15 years 8 months ago
Classification of Volatile Organic Compounds with Incremental SVMs and RBF Networks
Support Vector Machines (SVMs) have been applied to solve the classification of volatile organic compounds (VOC) data in some recent studies. SVMs provide good generalization perfo...
Zeki Erdem, Robi Polikar, Nejat Yumusak, Fikret S....
146
Voted
ICDM
2010
IEEE
187views Data Mining» more  ICDM 2010»
15 years 21 days ago
Financial Forecasting with Gompertz Multiple Kernel Learning
Financial forecasting is the basis for budgeting activities and estimating future financing needs. Applying machine learning and data mining models to financial forecasting is both...
Han Qin, Dejing Dou, Yue Fang
146
Voted
IWMMDBMS
1998
139views more  IWMMDBMS 1998»
15 years 4 months ago
Fusion of Multimedia Information
: In recent years the fusion of multimedia information from multiple real-time sources and databases has become increasingly important because of its practical significance in many...
Shi-Kuo Chang, Taieb Znati
131
Voted
PERCOM
2007
ACM
15 years 9 months ago
Sensor Web Design Studies for Realtime Dynamic Congestion Pricing
Traffic Congestion is a multi-billion dollar national problem and worsening every year with population growth and increase in freight traffic. We present a model for realistic s...
Milton Halem, Anand Patwardhan, Sandor Dornbush, M...