By Partha Niyogi
Between different themes, The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar brings jointly vital yet very diversified studying difficulties in the comparable analytical framework. the 1st issues the challenge of studying sensible mappings utilizing neural networks, by means of studying usual language grammars within the rules and parameters culture of Chomsky.
those studying difficulties are doubtless very assorted. Neural networks are real-valued, infinite-dimensional, non-stop mappings. nonetheless, grammars are boolean-valued, finite-dimensional, discrete (symbolic) mappings. additionally the study groups that paintings within the components nearly by no means overlap.
The book's target is to bridge this hole. It makes use of the formal suggestions built in statistical studying idea and theoretical laptop technology over the past decade to research either types of studying difficulties. via asking an identical query - how a lot details does it take to benefit? - of either difficulties, it highlights their similarities and ameliorations. particular effects comprise version choice in neural networks, lively studying, language studying and evolutionary types of language switch.
The Informational Complexity of studying: views on Neural Networks and Generative Grammar is a really interdisciplinary paintings. someone attracted to the interplay of desktop technological know-how and cognitive technology may still benefit from the publication. Researchers in synthetic intelligence, neural networks, linguistics, theoretical desktop technology, and information will locate it fairly proper.
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