|
Showing 1 - 2 of
2 matches in All Departments
Among other topics, The Informational Complexity of Learning:
Perspectives on Neural Networks and Generative Grammar brings
together two important but very different learning problems within
the same analytical framework. The first concerns the problem of
learning functional mappings using neural networks, followed by
learning natural language grammars in the principles and parameters
tradition of Chomsky. These two learning problems are seemingly
very different. Neural networks are real-valued,
infinite-dimensional, continuous mappings. On the other hand,
grammars are boolean-valued, finite-dimensional, discrete
(symbolic) mappings. Furthermore the research communities that work
in the two areas almost never overlap. The book's objective is to
bridge this gap. It uses the formal techniques developed in
statistical learning theory and theoretical computer science over
the last decade to analyze both kinds of learning problems. By
asking the same question - how much information does it take to
learn? - of both problems, it highlights their similarities and
differences. Specific results include model selection in neural
networks, active learning, language learning and evolutionary
models of language change. The Informational Complexity of
Learning: Perspectives on Neural Networks and Generative Grammar is
a very interdisciplinary work. Anyone interested in the interaction
of computer science and cognitive science should enjoy the book.
Researchers in artificial intelligence, neural networks,
linguistics, theoretical computer science, and statistics will find
it particularly relevant.
Among other topics, The Informational Complexity of Learning:
Perspectives on Neural Networks and Generative Grammar brings
together two important but very different learning problems within
the same analytical framework. The first concerns the problem of
learning functional mappings using neural networks, followed by
learning natural language grammars in the principles and parameters
tradition of Chomsky. These two learning problems are seemingly
very different. Neural networks are real-valued,
infinite-dimensional, continuous mappings. On the other hand,
grammars are boolean-valued, finite-dimensional, discrete
(symbolic) mappings. Furthermore the research communities that work
in the two areas almost never overlap. The book's objective is to
bridge this gap. It uses the formal techniques developed in
statistical learning theory and theoretical computer science over
the last decade to analyze both kinds of learning problems. By
asking the same question - how much information does it take to
learn? - of both problems, it highlights their similarities and
differences. Specific results include model selection in neural
networks, active learning, language learning and evolutionary
models of language change. The Informational Complexity of
Learning: Perspectives on Neural Networks and Generative Grammar is
a very interdisciplinary work. Anyone interested in the interaction
of computer science and cognitive science should enjoy the book.
Researchers in artificial intelligence, neural networks,
linguistics, theoretical computer science, and statistics will find
it particularly relevant.
|
|