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  • convolutional neural network

A powerful and flexible machine-learning approach that can be used in machine vision to help solve difficult problems. Inspired by biological processes, multiple layers of neurons process portions of an image to arrive at a classification model. The network of neurons is trained by a set of input images and the output classification (e.g., picture A is of a dog, picture B is of a cat, etc.) and the algorithm trains the neuron connection weights to arrive close to the desired classification. At run time, new images go through the network of neurons and produce probability of each classification. Their major advantage over traditional algorithms is their flexibility, accuracy and independence of human effort to design and specify the features. The training stage is very compute-intensive and requires a very large set of classified images to be accurate.
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