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» Multiple Layer Perceptron training using genetic algorithms
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104
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FLAIRS
2004
14 years 11 months ago
Iterative Improvement of Neural Classifiers
A new objective function for neural net classifier design is presented, which has more free parameters than the classical objective function. An iterative minimization technique f...
Jiang Li, Michael T. Manry, Li-min Liu, Changhua Y...
KDD
2009
ACM
178views Data Mining» more  KDD 2009»
15 years 10 months ago
Constrained optimization for validation-guided conditional random field learning
Conditional random fields(CRFs) are a class of undirected graphical models which have been widely used for classifying and labeling sequence data. The training of CRFs is typicall...
Minmin Chen, Yixin Chen, Michael R. Brent, Aaron E...
MCS
2007
Springer
15 years 3 months ago
Classifier Combining Rules Under Independence Assumptions
Classifier combining rules are designed for the fusion of the results from the component classifiers in a multiple classifier system. In this paper, we firstly propose a theoretica...
Shoushan Li, Chengqing Zong
81
Voted
CVPR
2007
IEEE
15 years 11 months ago
Compositional Boosting for Computing Hierarchical Image Structures
In this paper, we present a compositional boosting algorithm for detecting and recognizing 17 common image structures in low-middle level vision tasks. These structures, called &q...
Tianfu Wu, Gui-Song Xia, Song Chun Zhu
MIR
2010
ACM
207views Multimedia» more  MIR 2010»
14 years 8 months ago
Learning to rank for content-based image retrieval
In Content-based Image Retrieval (CBIR), accurately ranking the returned images is of paramount importance, since users consider mostly the topmost results. The typical ranking st...
Fabio F. Faria, Adriano Veloso, Humberto Mossri de...