Author: Leslie Valiant
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Publisher: Basic Books
Binding: Hardcover
ISBN: 0465032710
Category: Programming
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Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World
From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns. Programming books Probably Approximately Correct pdf. BR>
How does life prosper in a complex and erratic world? While we know that nature follows patterns—such as the law of gravity—our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?
In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. Download books Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World pdf via mediafire, 4shared, rapidshare.
Price comparison for Probably Approximately Correct: Nature's Algorithms for Learning and Prospering in a Complex World (Hardcover)
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From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns. How does life prosper in a complex and erratic world? While we know that nature follows patterns?such as the law of gravity?our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it? In Probably Approximately Correct , computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only su
From a leading computer scientist, a unifying theory that will revolutionize our understanding of how life evolves and learns. How does life prosper in a complex and erratic world? While we know that nature follows patterns?such as the law of gravity?our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it? In Probably Approximately Correct , computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only su
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Contributors: Leslie Valiant - Author. Format: Hardcover
Contributors: Leslie Valiant - Author. Format: Hardcover
Price: $55
This diagram illustrates the relationship between the approximation and estimation errors. According to PAC Learning theory, the estimation error quantifies how much we can “trust” the empirical risk minimization process to select a model close to the best in a given class. In computational learning theory, Probably Approximately Correct (PAC) Learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant who won the ACM AM Turing Award in 2010.
This diagram perfectly bounds with one of our favorite von Neumann's quotes. John von Neumann (1
This diagram illustrates the relationship between the approximation and estimation errors. According to PAC Learning theory, the estimation error quantifies how much we can “trust” the empirical risk minimization process to select a model close to the best in a given class. In computational learning theory, Probably Approximately Correct (PAC) Learning is a framework for mathematical analysis of machine learning. It was proposed in 1984 by Leslie Valiant who won the ACM AM Turing Award in 2010.
This diagram perfectly bounds with one of our favorite von Neumann's quotes. John von Neumann (1
Price: $99
This book constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, held in Canberra, Australia, in October 2010, co-located with the 13th International Conference on Discovery Science, DS 2010. The 26 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 44 submissions. The papers are divided into topical sections of papers on statistical learning; grammatical inference and graph learning; probably approximately correct learning; query learning and algorithmic teaching; o
This book constitutes the refereed proceedings of the 21th International Conference on Algorithmic Learning Theory, ALT 2010, held in Canberra, Australia, in October 2010, co-located with the 13th International Conference on Discovery Science, DS 2010. The 26 revised full papers presented together with the abstracts of 5 invited talks were carefully reviewed and selected from 44 submissions. The papers are divided into topical sections of papers on statistical learning; grammatical inference and graph learning; probably approximately correct learning; query learning and algorithmic teaching; o
Price: $41.54
This is a self contained volume in which the authors concentrate on the 'probably approximately correct model'. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
This is a self contained volume in which the authors concentrate on the 'probably approximately correct model'. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.
Download Probably Approximately Correct
BR>
How does life prosper in a complex and erratic world? While we know that nature follows patterns—such as the law of gravity—our everyday lives are beyond what known science can predict. We nevertheless muddle through even in the absence of theories of how to act. But how do we do it?
In Probably Approximately Correct, computer scientist Leslie Valiant presents a masterful synthesis of learning and evolution to show how both individually and collectively we not only survive, but prosper in a world as complex as our own. The key is “probably approximately correct” algorithms, a concept Valiant developed to explain how effective behavior can be learned. The model shows that pragmatically coping with a problem can provide a satisfactory solution in the absence of any theory of the problem. After all, finding a mate does not require a theory of mating. Valiant’s theory reveals the shared computational nature of evolution and learning, and sheds light on perennial questions such as nature versus nurture and the limits of artificial intelligence.
Offering a powerful and elegant model that encompasses life’s complexity, Probably Approximately Correct has profound implications for how we think about behavior, cognition, biological evolution, and the possibilities and limits of human and machine intelligence.
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