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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to;
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to;
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of machine learning. The book covers both key conformal predictors and the mathematical analysis of their properties. Algorithmic Learning in a;
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This new book-the first of its kind-examines the use of algorithmic techniques to compress random and non-random sequential strings found;
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This new book-the first of its kind-examines the use of algorithmic techniques to compress random and non-random sequential strings found;
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This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high;
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Martin-Lof level of randomness as found in random sequential binary strings. A classical study of statistics that addresses both a fundamental;
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This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB;
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This book examines the intelligent random walk algorithms based on learning automata: these versions of random walk algorithms gradually;
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Understand the fundamentals of algorithmic trading to apply algorithms to real market data and analyze the results of real-world trading;
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deploying automated algorithmic trading strategies, this book will help you level the playing field. Set up a proper Python environment for;
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fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated;
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submissions. The papers deal with emerging important algorithmic problems with a focus on the fundamental background, theoretical technology;
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Build a strong foundation of machine learning algorithms in 7 days Key Features Use Python and its wide array of machine learning libraries;
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fundamental algorithmic robotics (e.g., planning, learning, navigation, control, manipulation, optimality, completeness, and complexity) demonstrated;
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This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari;
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resource presents: a detailed look at the application of random graphs to pattern recognition; extensive examples of applications of the graphs;
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be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method;
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be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method;
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professionals who need a quick introduction but practical introduction how to deal with random signals and processes;
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principles of algorithmic art * Brief Processing language tutorial * Establishing artistic ground rules for a new work * Using random, semi;
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Key Features Think critically about data and use it to form and test a hypothesis Choose an appropriate machine learning model and train;
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such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and;
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such as Support Vector Machine, Neural Networks, Random Forest, K-Nearest Neighbors, Extreme Learning Machine, Deep Learning Approaches and;
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introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the;
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