Name
Abstract | ||
|---|---|---|
ANNS are efficient and objective classification methods in subject classification. It is an information processing system whose design was inspired by the structure and functioning of neuron in biology. Thus, they have been successfully applied to the numerous classification fields. Sometimes, however, classifications do not match the real world, and are subjected to errors. These problems are caused by the nature of artificial neural networks. By studying of these problems, it helps us to have a better understanding on ANNS classification and find a way to improve their performance. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/NCM.2009.118 | NCM |
Keywords | Field | DocType |
information processing system,subject,artificial neural networks classification,better understanding,pattern classification,neuron,anns classification,multilayer perceptrons,subject classification,error,anns,objective classification method,multilayer perceptron neural networks,artificial neural network,classification,real world,classification error,numerous classification field,data mining,artificial neural networks,classification algorithms,information processing,mathematical model | Computer science,Information processor,Multilayer perceptron neural network,Artificial intelligence,Statistical classification,Artificial neural network,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-3769-6 | 0 | 0.34 |
References | Authors | |
1 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihua Feng | 1 | 1 | 1.04 |
| Jiahong Feng | 2 | 0 | 0.34 |