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Feed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very di erent starting points of view. In this work a new learning model for FNN is proposed such that, in the linearly separable case, it tends to obtain the same solution as SVM. The key idea of the model is a weighting of the sum-of-squares error function, which is inspired by the AdaBoost algorithm. As in SVM, the hardness of the margin can be controlled, so that thisdoi:10.1016/j.neucom.2003.10.011 fatcat:umszhcqwz5dzzmutcnxbd3gvte