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Some of the fundamental constraints of automated machine vision
have been the inability to automatically adapt parameter settings
or utilize previous adaptations in changing environments. Symbolic
Visual Learning presents research which adds visual learning
capabilities to computer vision systems. Using this
state-of-the-art recognition technology, the outcome is different
adaptive recognition systems that can measure their own
performance, learn from their experience and outperform
conventional static designs. Written as a companion volume to Early
Visual Learning (edited by S. Nayar and T. Poggio), this book is
intended for researchers and students in machine vision and machine
learning.
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