Paper Title:
Classification of Area Flowing Based on KRNN Method
  Abstract

It is hard to search the influence variables and to classify the flowing areas of graduate employment due to the complex factor inputs. Recently the neural network method has been successfully employed to solve the problem. However the classification result is not ideal due to the nonlinearity and noise. In this work, by combining Recurrent Neural Network (RNN) with Kernel Principal Component Analysis (KPCA), a KRNN model is presented, based on which, the flowing areas of graduate employment is tried to be classified, and the complex factor problem has been well dealt with. In the model, RNN with Kernel Principal Component Analysis (KPCA) and Principal Component Analysis (PCA) as the feature extraction is introduced in as comparison. And then by an empirical study with actual data, it is shown that the proposed methods can both achieve good classification performance comparing with NN method. And the Kernel Principal Component Analysis method performs better than the Principal Component Analysis method.

  Info
Periodical
Advanced Materials Research (Volumes 255-260)
Edited by
Jingying Zhao
Pages
2855-2859
DOI
10.4028/www.scientific.net/AMR.255-260.2855
Citation
X. Sun, "Classification of Area Flowing Based on KRNN Method", Advanced Materials Research, Vols. 255-260, pp. 2855-2859, 2011
Online since
May 2011
Authors
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Price
$32.00
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