VIEW~~~~~~~~~~~~~~ ~ Communicated by Lawrence Jackel Neural Networks and the Bias/Variance Dilemma

Feedforward neural networks trained by error backpropagation are ex-amples of nonparametric regression estimators. We present a tutorial on nonparametric inference and its relation to neural networks, and we use the statistical viewpoint to highlight strengths and weaknesses of neural models. We illustrate the main points with some recognition experiments involving artificial data as well as handwritten numer-als. In way of conclusion, we suggest that current-generation feed-forward neural networks are largely inadequate for difficult problems in machine perception and machine learning, regardless of parallel-versus-serial hardware or other implementation issues. Furthermore, we suggest that the fundamental challenges in neural modeling are about representation rather than learning per se. This last point is supported by additional experiments with handwritten numerals. 1 Introduction _____________________ _ Much of the recent work on feedforward artificial neural networks bring