wyszukanych pozycji: 3
An Elementary Introduction to Statistical Learning Theory
ISBN: 9780470641835 / Angielski / Twarda / 2011 / 232 str. Termin realizacji zamówienia: ok. 22 dni roboczych. A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning
A joint endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory is a comprehensive and accessible primer on the rapidly evolving fields of statistical pattern recognition and statistical learning theory. Explaining these areas at a level and in a way that is not often found in other books on the topic, the authors present the basic theory behind... A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning
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558,79 zł |
Universal Estimation of Information Measures for Analog Sources
ISBN: 9781601982308 / Angielski / Miękka / 2009 / 104 str. Termin realizacji zamówienia: ok. 13-18 dni roboczych. Entropy, mutual information and divergence measure the randomness, dependence and dissimilarity, respectively, of random objects. In addition to their prominent role in information theory, they have found numerous applications, among others, in probability theory statistics, physics, chemistry, molecular biology, ecology, bioinformatics, neuroscience, machine learning, linguistics, and finance. Many of these applications require a universal estimate of information measures which does not assume knowledge of the statistical properties of the observed data. Over the past few decades, several...
Entropy, mutual information and divergence measure the randomness, dependence and dissimilarity, respectively, of random objects. In addition to their...
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411,83 zł |
Reliable Reasoning: Induction and Statistical Learning Theory
ISBN: 9780262517348 / Angielski / Miękka / 2012 / 120 str. Termin realizacji zamówienia: ok. 13-18 dni roboczych. In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science can benefit from statistical learning theory (SLT), the theory that lies behind recent advances in machine learning. The philosophical problem of induction, for example, is in part about the reliability of inductive reasoning, where the reliability of a method is measured by its statistically expected percentage of errors -- a central topic in SLT. After discussing philosophical attempts to evade the problem of induction, Harman and... In Reliable Reasoning, Gilbert Harman and Sanjeev Kulkarni -- a philosopher and an engineer -- argue that philosophy and cognitive science c... |
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157,71 zł |