Title :
The equivalency between a decision tree for classification and a feedback neural network
Author :
Aijun, Li ; Siwei, Luo ; Yunhui, Liu ; Hanbin, Yu
Author_Institution :
Beijing Jiao Tong Univ., China
fDate :
31 Aug.-4 Sept. 2004
Abstract :
In machine learning, the learning paradigms of an artificial neural network (ANN) and a decision tree (DT) are different, but they are equivalent in essence. This paper proves the approximate equivalency between feedback neural networks and decision trees. The result provides us a very useful guideline when we perform theoretical research and applications on DT and ANN.
Keywords :
decision trees; equivalence classes; interpolation; learning (artificial intelligence); pattern classification; recurrent neural nets; approximate equivalency; artificial neural network; decision tree; feedback neural network; machine learning; pattern classification; Artificial neural networks; Classification tree analysis; Decision trees; Electronic mail; Inference algorithms; Interpolation; Neural networks; Neurofeedback; Partial response channels; Partitioning algorithms;
Conference_Titel :
Signal Processing, 2004. Proceedings. ICSP '04. 2004 7th International Conference on
Print_ISBN :
0-7803-8406-7
DOI :
10.1109/ICOSP.2004.1441626
