主讲人简 介：Larry Yaeger is a scientist, programmer, and educator who has made innovative contributions in the fields of computational fluid dynamics, computer graphics, neural networks, handwriting recognition, and artificial life. He has recently returned to industry to develop and deploy applied machine learning techniques at Google.
内 容摘 要：The lecturer will discuss the combination of artificial neural network (ANN) character classification, a large- coverage, loosely-applied language model, and context-driven search over character segmentation, word segmentation, and word recognition hypotheses that made the Newton's Print Recognizer actually work. He will also discuss issues related to recognizer training, generalization, segmentation, language models, geometric context, probabilistic formalisms, etc., that had to be addressed and resolved in order to obtain sufficient recognition accuracy to be genuinely usable. Additionally, he will present some unique innovations in the application of ANNs to the problem of character classification for word recognition, including multiple representations, normalized output error, negative training, stroke warping, frequency balancing, and error emphasis, all of which are interpretable as methods for reducing prior biases in the training data, and all of which significantly improve the performance of the network classifier when embedded in such a system. Other practical issues, such as quantized (one-byte) weights for the neural network and dictionary stemming will also be addressed.
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