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IJCNN
2008
IEEE
15 years 7 months ago
Evolving a neural network using dyadic connections
—Since machine learning has become a tool to make more efficient design of sophisticated systems, we present in this paper a novel methodology to create powerful neural network ...
Andreas Huemer, Mario A. Góngora, David A. ...
CORR
2010
Springer
177views Education» more  CORR 2010»
15 years 25 days ago
Supervised Random Walks: Predicting and Recommending Links in Social Networks
Predicting the occurrence of links is a fundamental problem in networks. In the link prediction problem we are given a snapshot of a network and would like to infer which interact...
Lars Backstrom, Jure Leskovec
115
Voted
JMLR
2008
100views more  JMLR 2008»
15 years 21 days ago
Hit Miss Networks with Applications to Instance Selection
In supervised learning, a training set consisting of labeled instances is used by a learning algorithm for generating a model (classifier) that is subsequently employed for decidi...
Elena Marchiori
113
Voted
RECOMB
2005
Springer
16 years 1 months ago
The Factor Graph Network Model for Biological Systems
Abstract. We introduce an extended computational framework for studying biological systems. Our approach combines formalization of existing qualitative models that are in wide but ...
Irit Gat-Viks, Amos Tanay, Daniela Raijman, Ron Sh...
140
Voted
JMLR
2008
230views more  JMLR 2008»
15 years 21 days ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...