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The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution . The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. Abstract. This thesis is a study of the computational complexity of machine learning from examples in the distribution-free model introduced by L. G. Valiant (V84). In the distribution-free model, a learning .

Computational Complexity of Machine Learning | Michael J. Kearns | download
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The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. ACM distinguished. Mathematics, Computer Science. This thesis is a study of the computational complexity of machine learning from examples in the distribution-free model introduced by L. G. Valiant (V84). In the distribution-free model, a learning . The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution .

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The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. Oct 22,  · The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for blogger.com by: The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution .

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The computational complexity of learning Boolean concepts from examples is investigated. It is shown for various classes of concept representations that these cannot be learned feasibly in a distribution . ACM distinguished. Mathematics, Computer Science. This thesis is a study of the computational complexity of machine learning from examples in the distribution-free model introduced by L. G. Valiant (V84). In the distribution-free model, a learning . Oct 22,  · The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for blogger.com by:

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ACM distinguished. Mathematics, Computer Science. This thesis is a study of the computational complexity of machine learning from examples in the distribution-free model introduced by L. G. Valiant (V84). In the distribution-free model, a learning . The Computational Complexity of Machine Learning is a mathematical study of the possibilities for efficient learning by computers. It works within recently introduced models for machine inference that are based on the theory of computational complexity and that place an explicit emphasis on efficient and general algorithms for learning. Abstract. This thesis is a study of the computational complexity of machine learning from examples in the distribution-free model introduced by L. G. Valiant (V84). In the distribution-free model, a learning .