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NN
2006
Springer
153views Neural Networks» more  NN 2006»
13 years 7 months ago
An incremental network for on-line unsupervised classification and topology learning
This paper presents an on-line unsupervised learning mechanism for unlabeled data that are polluted by noise. Using a similarity thresholdbased and a local error-based insertion c...
Shen Furao, Osamu Hasegawa
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
CIKM
2009
Springer
14 years 2 months ago
Privacy-enhanced public view for social graphs
We consider the problem of releasing a limited public view of a sensitive graph which reveals at least k edges per node. We are motivated by Facebook’s public search listings, w...
Hyoungshick Kim, Joseph Bonneau
ACE
2004
186views Education» more  ACE 2004»
13 years 9 months ago
TinkerNet: A Low-Cost Networking Laboratory
The 2002 SIGCOMM Workshop on Educational Challenges for Computer Networking [Kur02a] exposed many issues related to teaching computer networking with the need for a laboratory in ...
Michael Erlinger, Mart Molle, Titus Winters, Chris...
VISUALIZATION
2005
IEEE
14 years 1 months ago
Opening the Black Box - Data Driven Visualization of Neural Network
Arti cial neural networks are computer software or hardware models inspired by the structure and behavior of neurons in the human nervous system. As a powerful learning tool, incr...
Fan-Yin Tzeng, Kwan-Liu Ma