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N.P. Archer, S. Wang, "Learning Bias in Neural Networks and an Approach to Controlling Its Effect in Monotonic Classification," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 15, no. 9, pp. 962966, September, 1993.  
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@article{ 10.1109/34.232084, author = {N.P. Archer and S. Wang}, title = {Learning Bias in Neural Networks and an Approach to Controlling Its Effect in Monotonic Classification}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {15}, number = {9}, issn = {01628828}, year = {1993}, pages = {962966}, doi = {http://doi.ieeecomputersociety.org/10.1109/34.232084}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  Learning Bias in Neural Networks and an Approach to Controlling Its Effect in Monotonic Classification IS  9 SN  01628828 SP962 EP966 EPD  962966 A1  N.P. Archer, A1  S. Wang, PY  1993 KW  boundary generation unpredictability; monotonic classification; backpropagation training algorithm; learning bias; pattern recognition; multiarchitecture monotonic function neural networks; backpropagation; learning (artificial intelligence); neural nets; pattern recognition VL  15 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
As a learning machine, a neural network using the backpropagation training algorithm is subject to learning bias. This results in unpredictability of boundary generation behavior in pattern recognition applications, especially in the case of small training sample size. It is suggested that in a large class of pattern recognition problems such as managerial and other problems possessing monotonicity properties, the effect of learning bias can be controlled by using multiarchitecture monotonic function neural networks.
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