15 search results - page 3 / 3 » Maximum Margin Learning with Incomplete Data: Learning Netwo... |
CVPR
2008
IEEE
14 years 6 months ago
2008
IEEE
Graphical models such as Bayesian Networks (BNs) are being increasingly applied to various computer vision problems. One bottleneck in using BN is that learning the BN model param...
PRL
2011
12 years 11 months ago
2011
In some real world applications, such as spectrometry, functional models achieve better predictive performances if they work on the derivatives of order m of their inputs rather t...
ICONIP
2007
13 years 6 months ago
2007
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...
JMLR
2008
13 years 4 months ago
2008
The linear model with sparsity-favouring prior on the coefficients has important applications in many different domains. In machine learning, most methods to date search for maxim...
NN
1998
Springer
13 years 4 months ago
1998
Springer
The number of required hidden units is statistically estimated for feedforward neural networks that are constructed by adding hidden units one by one. The output error decreases w...