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KDD
2003
ACM
175views Data Mining» more  KDD 2003»
16 years 4 months ago
Time and sample efficient discovery of Markov blankets and direct causal relations
Data Mining with Bayesian Network learning has two important characteristics: under broad conditions learned edges between variables correspond to causal influences, and second, f...
Ioannis Tsamardinos, Constantin F. Aliferis, Alexa...
150
Voted
ESANN
2006
15 years 5 months ago
Magnification control for batch neural gas
Neural gas (NG) constitutes a very robust clustering algorithm which can be derived as stochastic gradient descent from a cost function closely connected to the quantization error...
Barbara Hammer, Alexander Hasenfuss, Thomas Villma...
118
Voted
IJCNN
2007
IEEE
15 years 10 months ago
Spectral Clustering of Synchronous Spike Trains
— In this paper a clustering algorithm that learns the groups of synchronized spike trains directly from data is proposed. Clustering of spike trains based on the presence of syn...
António R. C. Paiva, Sudhir Rao, Il Park, J...
ESOA
2006
15 years 7 months ago
Greedy Cheating Liars and the Fools Who Believe Them
Evolutionary algorithms based on "tags" can be adapted to induce cooperation in selfish environments such as peer-to-peer systems. In this approach, nodes periodically co...
Stefano Arteconi, David Hales, Özalp Babaoglu
137
Voted
ICONIP
2008
15 years 5 months ago
On Node-Fault-Injection Training of an RBF Network
Abstract. While injecting fault during training has long been demonstrated as an effective method to improve fault tolerance of a neural network, not much theoretical work has been...
John Sum, Chi-Sing Leung, Kevin Ho