CB3: An Adaptive Error Function for Backpropagation Training

Effective backpropagation training of multi-layer perceptrons depends on the incorporation of an appropriate error or objective function. Classification-based (CB) error functions are heuristic approaches that attempt to guide the network directly to correct pattern classification rather than using common error minimization heuristics, such as sum-squared error and cross-entropy, which do not explicitly minimize classification error. This work presents CB3, a novel CB approach that learns the error function to be used while training. This is accomplished by learning pattern confidence margins during training, which are used to dynamically set output target values for each training pattern. On eleven applications, CB3 significantly outperforms previous CB error functions, and also reduces

• Type: Journal
• Year: 2006
• Where: NPL
• Authors: Michael Rimer, Tony Martinez
• Keywords: Appropriate Error, Error Functions, Error Minimization Heuristics, NPL 2006