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An Elementary Introduction to Statistical Learning Theory

A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint;

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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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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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Statistical Mechanics and Thermodynamics

A textbook on statistical mechanics and thermodynamics, suitable for a graduate-level physics course. Covers Boltzmann systems, quantum;

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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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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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Multiscale Signal Analysis and Modeling

statistical learning theory.;

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Algorithmic Learning Theory

complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization;

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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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Algorithmic Learning Theory

organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive;

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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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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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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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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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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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Rank-Based Methods for Shrinkage and Selection

Rank-Based Methods for Shrinkage and Selection A practical and hands-on guide to the theory and methodology of statistical estimation based;

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Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;

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Introduction to Statistical Decision Theory

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision;

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Biostatistics for Clinical and Public Health Research

statistical software is illustrated in full, including how to interpret results. Focusing on statistical models without all the theory, the book;

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Biostatistics for Clinical and Public Health Research

statistical software is illustrated in full, including how to interpret results. Focusing on statistical models without all the theory, the book;

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Introduction to Statistical Machine Learning

Machine learning allows computers to learn and discern patterns without actually being programmed. When Statistical techniques and;

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Machine Learning: Theory and Applications

Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size;

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Machine Learning Theoretical Foundations and Practical Applications

in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network;

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Applied Statistical Theory & Applications

, compares many statistical models, and includes a theory that is oriented to real life problems.;

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