Description: This applications-oriented book presents, for the first time, Learning-Generalization-Seeing-Recognition Hybrids. Numerous new learning algorithms are described, including holographic networks, adaptive decoupled momentum, feature construction, second-order gradient, and adaptive-symbolic methods. Object recognition systems in real-time applications are presented and include massively parallel and systolic array implementations. These systems exhibit up to 2 billion operations and over 300 billion connections per second. Position, scale and rotation invariant systems for industrial machine vision are presented, including testing of IC chips; flying object recognition; space shuttle and aircraft experiments; detection of moving objects; shape recognition in manufacturing; recognition of occluded objects; biomedical image classification; three-dimensional ultrasonic imaging in clinical ophthalmology, and others. New invariant object recognition paradigms include orthogonal sets of feature layers; higher-order neural networks; detection of movement-attention-tracking; landmark matching; segmentation of three-dimensional images; dynamic links on the reduced mesh of trees. Fast Learning and Invariant Object Recognition presents a unified treatment of material that has previously been scattered worldwide in a number of research reports, as well as previously unpublished methods and results from the IRIS (Integration of Reasoning, Informing and Serving) Group.

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