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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 thedoi:10.1007/s11063-006-9014-9 fatcat:7aslkmbjszhffeaysfzlfft4na