By Harry Wechsler
ISBN-10: 0127412514
ISBN-13: 9780127412511
Those volumes discover fresh examine in neural networks that has complicated our realizing of human and computing device notion. Contributions from foreign researchers handle either theoretical and sensible concerns with regards to the feasibility of neural community versions to give an explanation for human belief and enforce desktop conception. quantity 1 covers types for knowing human notion by way of allotted computation in addition to examples of neural community types for laptop conception and quantity 2 examines computational and adaptational difficulties with regards to using neural structures and discusses the corresponding architectures had to enforce neural networks for belief
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Additional resources for Neural Networks for Perception. Human and Machine Perception
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Mental images and their mations. MIT Press, Cambridge, MA, 1982. [13] D. Marr. Vision. W. H. Freeman, San Francisco, CA, 1982. transfor 40 S. Edelman [14] Τ. Poggio and F. Girosi. Regularization algorithms for learning that are equivalent to multilayer networks. Science, 247:978-982, 1990. [15] T. Poggio and S. Edelman. A network that learns to recognize threedimensional objects. Nature, 343:263-266, 1990. [16] H. B. Barlow. Cerebral cortex as model builder. In D. Rose and V. G. Dobson, editors, Models of the visual cortex, pages 37-46.
The main requirement imposed on the representation of an individual view in the CLF model is that of compactness. In principle, there is no reason why a view should not be jointly represented by a substantial pro portion of the second-layer units (see Figure 4). 2 A Network Model of Object Recognition in Human Vision 33 together more efficiently to form representations of entire objects. 2 Learning Object Representations The CLF model acquires the representation of a novel object as follows. The very first view of the object is allocated a representation unit in the second layer through projection convergence, followed by non-maximum suppression.
On Comput. Vision Pattern Recognition. 11. E. J. (1986), "Learning and Relearning in Boltzmann Machines," in Parallel Distributed Processing, Explorations in the Microstructure of Cognition, Vol. E. ) MIT Press, Cambridge, Ma. N. Cooper 12. J. (1982), "Neural Networks and Physical Systems with Emergent Collective Computational Abilities," Proc. Natl, Acad, Sei. 79:2554-2558. 13. H. N. Wiesel (1961), "Integrative Action in the Cat's Lateral Geniculate Body," / . Physiol. 155: 385-398. 14. H. N. Wiesel (1962), "Receptive Fields, Binocular Interaction and Functional Architecture in the Cat's Visual Cortex.
Neural Networks for Perception. Human and Machine Perception by Harry Wechsler
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