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Foundations Of Rule Learning
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Book Synopsis Foundations of Rule Learning by : Johannes Fürnkranz
Download or read book Foundations of Rule Learning written by Johannes Fürnkranz and published by Springer Science & Business Media. This book was released on 2012-11-06 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: Rules – the clearest, most explored and best understood form of knowledge representation – are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.
Book Synopsis Foundations of Learning Classifier Systems by : Larry Bull
Download or read book Foundations of Learning Classifier Systems written by Larry Bull and published by Springer Science & Business Media. This book was released on 2005-07-22 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
Book Synopsis Rule Technologies: Foundations, Tools, and Applications by : Nick Bassiliades
Download or read book Rule Technologies: Foundations, Tools, and Applications written by Nick Bassiliades and published by Springer. This book was released on 2015-07-11 with total page 474 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 9th International RuleML Symposium, RuleML 2015, held in Berlin, Germany, in August 2015. The 25 full papers, 4 short papers, 2 full keynote papers, 2 invited research track overview papers, 1 invited paper, 1 invited abstracts presented were carefully reviewed and selected from 63 submissions. The papers cover the following topics: general RuleML track; complex event processing track, existential rules and datalog+/- track; legal rules and reasoning track; rule learning track; industry track.
Book Synopsis Foundations of Data Science by : Avrim Blum
Download or read book Foundations of Data Science written by Avrim Blum and published by Cambridge University Press. This book was released on 2020-01-23 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix norms and VC-dimension. This book is suitable for both undergraduate and graduate courses in the design and analysis of algorithms for data.
Book Synopsis Foundations of Machine Learning, second edition by : Mehryar Mohri
Download or read book Foundations of Machine Learning, second edition written by Mehryar Mohri and published by MIT Press. This book was released on 2018-12-25 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.
Book Synopsis Rules of Play by : Katie Salen Tekinbas
Download or read book Rules of Play written by Katie Salen Tekinbas and published by MIT Press. This book was released on 2003-09-25 with total page 680 pages. Available in PDF, EPUB and Kindle. Book excerpt: An impassioned look at games and game design that offers the most ambitious framework for understanding them to date. As pop culture, games are as important as film or television—but game design has yet to develop a theoretical framework or critical vocabulary. In Rules of Play Katie Salen and Eric Zimmerman present a much-needed primer for this emerging field. They offer a unified model for looking at all kinds of games, from board games and sports to computer and video games. As active participants in game culture, the authors have written Rules of Play as a catalyst for innovation, filled with new concepts, strategies, and methodologies for creating and understanding games. Building an aesthetics of interactive systems, Salen and Zimmerman define core concepts like "play," "design," and "interactivity." They look at games through a series of eighteen "game design schemas," or conceptual frameworks, including games as systems of emergence and information, as contexts for social play, as a storytelling medium, and as sites of cultural resistance. Written for game scholars, game developers, and interactive designers, Rules of Play is a textbook, reference book, and theoretical guide. It is the first comprehensive attempt to establish a solid theoretical framework for the emerging discipline of game design.
Book Synopsis Interpretable Machine Learning by : Christoph Molnar
Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Book Synopsis Patterns, Predictions, and Actions: Foundations of Machine Learning by : Moritz Hardt
Download or read book Patterns, Predictions, and Actions: Foundations of Machine Learning written by Moritz Hardt and published by Princeton University Press. This book was released on 2022-08-23 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: An authoritative, up-to-date graduate textbook on machine learning that highlights its historical context and societal impacts Patterns, Predictions, and Actions introduces graduate students to the essentials of machine learning while offering invaluable perspective on its history and social implications. Beginning with the foundations of decision making, Moritz Hardt and Benjamin Recht explain how representation, optimization, and generalization are the constituents of supervised learning. They go on to provide self-contained discussions of causality, the practice of causal inference, sequential decision making, and reinforcement learning, equipping readers with the concepts and tools they need to assess the consequences that may arise from acting on statistical decisions. Provides a modern introduction to machine learning, showing how data patterns support predictions and consequential actions Pays special attention to societal impacts and fairness in decision making Traces the development of machine learning from its origins to today Features a novel chapter on machine learning benchmarks and datasets Invites readers from all backgrounds, requiring some experience with probability, calculus, and linear algebra An essential textbook for students and a guide for researchers
Book Synopsis Understanding Machine Learning by : Shai Shalev-Shwartz
Download or read book Understanding Machine Learning written by Shai Shalev-Shwartz and published by Cambridge University Press. This book was released on 2014-05-19 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
Book Synopsis Rule Technologies. Research, Tools, and Applications by : Jose Julio Alferes
Download or read book Rule Technologies. Research, Tools, and Applications written by Jose Julio Alferes and published by Springer. This book was released on 2016-06-27 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 10th International RuleML Symposium, RuleML 2016, held in New York, NY, USA during July 2016. The 19 full papers, 1 short paper, 2 keynote abstracts, 2 invited tutorial papers, 1 invited standard paper, presented were carefully reviewed and selected from 36 submissions. RuleML is a leading conference aiming to build bridges between academia and industry in the field of rules and its applications, especially as part of the semantic technology stack. It is devoted to rule-based programming and rule-based systems including production rule systems, logic programming rule engines, and business rule engines and business rule management systems, Semantic Web rule languages and rule standards and technologies, and research on inference rules, transformation rules, decision rules, and ECA rules.
Book Synopsis Unsupervised Learning by : Geoffrey Hinton
Download or read book Unsupervised Learning written by Geoffrey Hinton and published by MIT Press. This book was released on 1999-05-24 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since its founding in 1989 by Terrence Sejnowski, Neural Computation has become the leading journal in the field. Foundations of Neural Computation collects, by topic, the most significant papers that have appeared in the journal over the past nine years. This volume of Foundations of Neural Computation, on unsupervised learning algorithms, focuses on neural network learning algorithms that do not require an explicit teacher. The goal of unsupervised learning is to extract an efficient internal representation of the statistical structure implicit in the inputs. These algorithms provide insights into the development of the cerebral cortex and implicit learning in humans. They are also of interest to engineers working in areas such as computer vision and speech recognition who seek efficient representations of raw input data.
Book Synopsis Rules and Reasoning by : Anna Fensel
Download or read book Rules and Reasoning written by Anna Fensel and published by Springer Nature. This book was released on 2023-11-15 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 7th International Joint Conference on Rules and Reasoning, RuleML+RR 2023, held in Oslo, Norway, during September 18–20, 2023. The 13 full papers and 3 short papers included in these proceedings were carefully reviewed and selected from 46 submissions. They focus on all aspects of theoretical advances; novel technologies; innovative applications; knowledge representation; reasoning with rules; and research, development, applications of rule-based systems.
Book Synopsis Machine Learning Foundations by : Taeho Jo
Download or read book Machine Learning Foundations written by Taeho Jo and published by Springer Nature. This book was released on 2021-02-12 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides conceptual understanding of machine learning algorithms though supervised, unsupervised, and advanced learning techniques. The book consists of four parts: foundation, supervised learning, unsupervised learning, and advanced learning. The first part provides the fundamental materials, background, and simple machine learning algorithms, as the preparation for studying machine learning algorithms. The second and the third parts provide understanding of the supervised learning algorithms and the unsupervised learning algorithms as the core parts. The last part provides advanced machine learning algorithms: ensemble learning, semi-supervised learning, temporal learning, and reinforced learning. Provides comprehensive coverage of both learning algorithms: supervised and unsupervised learning; Outlines the computation paradigm for solving classification, regression, and clustering; Features essential techniques for building the a new generation of machine learning.
Book Synopsis Reasoning Web. Learning, Uncertainty, Streaming, and Scalability by : Claudia d’Amato
Download or read book Reasoning Web. Learning, Uncertainty, Streaming, and Scalability written by Claudia d’Amato and published by Springer. This book was released on 2018-09-14 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains lecture notes of the 14th Reasoning Web Summer School (RW 2018), held in Esch-sur-Alzette, Luxembourg, in September 2018. The research areas of Semantic Web, Linked Data, and Knowledge Graphs have recently received a lot of attention in academia and industry. Since its inception in 2001, the Semantic Web has aimed at enriching the existing Web with meta-data and processing methods, so as to provide Web-based systems with intelligent capabilities such as context awareness and decision support. The Semantic Web vision has been driving many community efforts which have invested a lot of resources in developing vocabularies and ontologies for annotating their resources semantically. Besides ontologies, rules have long been a central part of the Semantic Web framework and are available as one of its fundamental representation tools, with logic serving as a unifying foundation. Linked Data is a related research area which studies how one can make RDF data available on the Web and interconnect it with other data with the aim of increasing its value for everybody. Knowledge Graphs have been shown useful not only for Web search (as demonstrated by Google, Bing, etc.) but also in many application domains.
Book Synopsis Artificial Intelligence XXXIX by : Max Bramer
Download or read book Artificial Intelligence XXXIX written by Max Bramer and published by Springer Nature. This book was released on 2022-12-05 with total page 373 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 42nd SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2022, which was held in Cambridge, UK, in December 2022. The 20 full papers and 11 short papers presented in this volume were carefully reviewed and selected from 40 submissions. The volume includes technical papers presenting new and innovative developments in the field as well as application papers presenting innovative applications of AI techniques in a number of subject domains. The papers are organized in the following topical sections: best technical paper; best application paper; AI for health and medicine; AI for scientific discovery and decision making; AI for industrial applications; feasibility studies of applied AI; and short papers.
Book Synopsis Data Mining and Machine Learning by : Mohammed J. Zaki
Download or read book Data Mining and Machine Learning written by Mohammed J. Zaki and published by Cambridge University Press. This book was released on 2020-01-30 with total page 780 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and graduate courses provides a comprehensive, in-depth overview of data mining, machine learning and statistics, offering solid guidance for students, researchers, and practitioners. The book lays the foundations of data analysis, pattern mining, clustering, classification and regression, with a focus on the algorithms and the underlying algebraic, geometric, and probabilistic concepts. New to this second edition is an entire part devoted to regression methods, including neural networks and deep learning.
Book Synopsis Discovery Science by : Petra Kralj Novak
Download or read book Discovery Science written by Petra Kralj Novak and published by Springer Nature. This book was released on 2019-10-18 with total page 555 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 22nd International Conference on Discovery Science, DS 2019, held in Split, Coratia, in October 2019. The 21 full and 19 short papers presented together with 3 abstracts of invited talks in this volume were carefully reviewed and selected from 63 submissions. The scope of the conference includes the development and analysis of methods for discovering scientific knowledge, coming from machine learning, data mining, intelligent data analysis, big data analysis as well as their application in various scientific domains. The papers are organized in the following topical sections: Advanced Machine Learning; Applications; Data and Knowledge Representation; Feature Importance; Interpretable Machine Learning; Networks; Pattern Discovery; and Time Series.
