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ECCV
2010
Springer
13 years 8 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof
ICDE
2012
IEEE
237views Database» more  ICDE 2012»
11 years 10 months ago
On Discovery of Traveling Companions from Streaming Trajectories
— The advance of object tracking technologies leads to huge volumes of spatio-temporal data collected in the form of trajectory data stream. In this study, we investigate the pro...
Lu An Tang, Yu Zheng, Jing Yuan, Jiawei Han, Alice...
KDD
2009
ACM
133views Data Mining» more  KDD 2009»
14 years 8 months ago
On the tradeoff between privacy and utility in data publishing
In data publishing, anonymization techniques such as generalization and bucketization have been designed to provide privacy protection. In the meanwhile, they reduce the utility o...
Tiancheng Li, Ninghui Li
KDD
2006
ACM
201views Data Mining» more  KDD 2006»
14 years 8 months ago
Clustering based large margin classification: a scalable approach using SOCP formulation
This paper presents a novel Second Order Cone Programming (SOCP) formulation for large scale binary classification tasks. Assuming that the class conditional densities are mixture...
J. Saketha Nath, Chiranjib Bhattacharyya, M. Naras...
IMC
2005
ACM
14 years 1 months ago
Network Anomography
Anomaly detection is a first and important step needed to respond to unexpected problems and to assure high performance and security in IP networks. We introduce a framework and ...
Yin Zhang, Zihui Ge, Albert G. Greenberg, Matthew ...