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KDD
2009
ACM
180views Data Mining» more  KDD 2009»
14 years 8 months ago
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
Active and semi-supervised learning are important techniques when labeled data are scarce. Recently a method was suggested for combining active learning with a semi-supervised lea...
Sofus A. Macskassy
MIR
2005
ACM
129views Multimedia» more  MIR 2005»
14 years 1 months ago
Multi-graph enabled active learning for multimodal web image retrieval
In this paper, we propose a multimodal Web image retrieval technique based on multi-graph enabled active learning. The main goal is to leverage the heterogeneous data on the Web t...
Xin-Jing Wang, Wei-Ying Ma, Lei Zhang, Xing Li
CASES
2006
ACM
13 years 11 months ago
Cost-efficient soft error protection for embedded microprocessors
Device scaling trends dramatically increase the susceptibility of microprocessors to soft errors. Further, mounting demand for embedded microprocessors in a wide array of safety c...
Jason A. Blome, Shantanu Gupta, Shuguang Feng, Sco...
DATE
2004
IEEE
175views Hardware» more  DATE 2004»
13 years 11 months ago
Breaking Instance-Independent Symmetries in Exact Graph Coloring
Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Graph coloring is...
Arathi Ramani, Fadi A. Aloul, Igor L. Markov, Kare...
ECCV
2010
Springer
13 years 7 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