3076 search results - page 301 / 616 » Active Learning for Networked Data |
144
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PAKDD
2000
ACM
15 years 10 months ago
2000
ACM
In order to discover relevant weights of neural networks, this paper proposes a novel method to learn a distinct squared penalty factor for each weight as a minimization problem ov...
178
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ICML
2008
IEEE
16 years 7 months ago
2008
IEEE
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
177
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ICML
2009
IEEE
16 years 7 months ago
2009
IEEE
Sparsity is a desirable property in high dimensional learning. The 1-norm regularization can lead to primal sparsity, while max-margin methods achieve dual sparsity. Combining the...
221
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SAC
2002
ACM
15 years 6 months ago
2002
ACM
We present a generic mobility and traffic generation framework that can be incorporated into a tool for modeling and simulating large scale ad hoc networks. Three components of thi...
172
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ML
2006
ACM
15 years 6 months ago
2006
ACM
Backpropagation, similar to most learning algorithms that can form complex decision surfaces, is prone to overfitting. This work presents classification-based objective functions, ...