1994
DOI: 10.1142/9789812797797_0011
|View full text |Cite
|
Sign up to set email alerts
|

An Adaptive Modular Neural Network With Application to Unconstrained Character Recognition

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1

Citation Types

0
4
0

Year Published

2001
2001
2006
2006

Publication Types

Select...
2
1

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(4 citation statements)
references
References 9 publications
0
4
0
Order By: Relevance
“…Mui et.al. [9] presented two important characteristics in NN: learning and generalization. Learning process associates with network architecture that will change the connection structure (13) between units and signal strength in the connection structure.…”
Section: Image Classificationsmentioning
confidence: 99%
“…Mui et.al. [9] presented two important characteristics in NN: learning and generalization. Learning process associates with network architecture that will change the connection structure (13) between units and signal strength in the connection structure.…”
Section: Image Classificationsmentioning
confidence: 99%
“…Neural networks are inherently parallel architectures, but can also properly be combined by exploiting their learning capabilities for implementing a decision function combining the outputs of different classifiers [13]. A parallel combination of classifiers entirely based on neural networks is described in [15], where a modular neural network is built for the recognition of unconstrained handwritten digits. The network consists of three subsequent stages:…”
Section: Connectionist-based Classifiersmentioning
confidence: 99%
“…For instance, in [20], the q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Output Error q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Output Error q q q q q q q q q q q q q q q q q q q q q q q q q q q q q Output Error q q q q q q q q q q q q q q recognition of the substructure shape in Kanji characters is carried out by first indexing structural features extracted from the character, and afterwards by verifying a hypothesized model using the corresponding neural network. A related approach is proposed in [15], where the verification modules are based on MLP classifiers. The autoassociator-based architecture is also based on a set of MLP for verification, but the neural networks are trained so as to force the output to follow the input value.…”
Section: Autoassociator-based Classifiermentioning
confidence: 99%
“…Mui et al [7] presented two important characteristics in NN: learning and generalization. Learning process associates with network architecture that will change the connection structure between units and signal strength in the connection structure.…”
Section: Nn Mathematical Model and Algorithmsmentioning
confidence: 99%