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The Nature of Statistical Learning Theory

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It;

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The Nature of Statistical Learning Theory

The aim of this book is to discuss the fundamental ideas which lie behind the statistical theory of learning and generalization. It;

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An Elementary Introduction to Statistical Learning Theory

endeavor from leading researchers in the fields of philosophy and electrical engineering, An Elementary Introduction to Statistical Learning Theory;

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Statistical Learning Theory

A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem;

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Mathematical Foundations of Nature-Inspired Algorithms

theory, statistical learning, and statistical measures. Students and researchers in optimization, operations research, artificial intelligence;

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Statistical and Machine Learning Approaches for Network Analysis

Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches;

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Machine Learning: A Practical Approach on the Statistical Learning Theory

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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Algebraic Geometry and Statistical Learning Theory

Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used;

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The Balancing Act

techniques, combining linguistic with statistical methods in automatic speech understanding, exploring the nature of transformation-based learning;

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Social Learning

, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social;

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Social Learning

, including statistical modeling of the spatial distribution of behavior traits. They also describe the latest theory and empirical findings on social;

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Model Selection and Error Estimation in a Nutshell

true error of the learned model based just on quantities computed on the available data. However, for a long time, Statistical learning theory;

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Statistical Learning from a Regression Perspective

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable;

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Algorithmic Learning Theory

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB;

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Algorithmic Learning Theory

This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari;

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Towards Intelligent Modeling: Statistical Approximation Theory

limit fails. The results of this book not only cover the classical and statistical approximation theory, but also are applied in the fuzzy logic;

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Machine Learning

This book presents the Statistical Learning Theory in a detailed and easy to understand way, by using practical examples, algorithms and;

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Machine Learning for Knowledge Discovery with R

: Contains statistical theory for the most recent supervised and unsupervised machine learning methodologies. Emphasizes broad statistical thinking;

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Theory Of Quantum Liquids

central concept of response functions. It is intended as a text for a graduate course in quantum statistical mechanics or low temperature theory.;

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Modern Course Statistical Physics 4th Ed

A Modern Course in Statistical Physics is a textbook that illustrates the foundations of equilibrium and non-equilibrium statistical;

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Theory of Quantum Liquids, Volume I

central concept of response functions. It is intended as a text for a graduate course in quantum statistical mechanics or low temperature theory.;

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Statistical Foundations of Econometric Modelling

is on the concepts and ideas underlying probability theory and statistical inference, and on motivating the learning of them both at a formal;

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Statistical Bioinformatics with R

Statistical Bioinformatics provides a balanced treatment of statistical theory in the context of bioinformatics applications.

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Self-Normalized Processes: Limit Theory and Statistical Applications

-statistic introduced in 1908 by Gosset, whose portrait is on the front cover. Due to the highly non-linear nature of these processes, the theory;

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Boosted Statistical Relational Learners

Learning (SRL) methods. These methods combine the expressiveness of first-order logic and the ability of probability theory to handle uncertainty;

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Statistical Application Development with R and Python -

spatial statistics. By the end of this book you will be able to apply your statistical learning in major domains at work or in your projects;

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Introduction to the Theory and Practice of Econometrics

a computer handbook. Presentation covers modern statistical models and focuses on the sampling theory process by which the data were;

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