Neural networks and the bias/variance dilemma
Neural Computation
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An Intelligent Inspection Engine (IIE) for classification of non-regular shaped objects from images is described and evaluated using real-world data from a waste package sorting application. The entire system is self-organizing. Principal component analysis and additional a priori knowledge on color properties are used for feature extraction. As classifiers growing neural networks provide robustness and minimize the number of runs for parameter tuning. We propose a method to encompass feature extraction and classification within a bootstrap procedure. These method reduces the immense memory requirement for the computation of principal components if number and size of training images are huge without to much loss of recognition quality.