Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Logical and Relational Learning PDF full book. Access full book title Logical and Relational Learning by Luc De Raedt. Download full books in PDF and EPUB format.
Author: Luc De Raedt Publisher: Springer Science & Business Media ISBN: 3540688560 Category : Computers Languages : en Pages : 395
Book Description
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Author: Luc De Raedt Publisher: Springer Science & Business Media ISBN: 3540688560 Category : Computers Languages : en Pages : 395
Book Description
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Author: Luc De Raedt Publisher: Springer Science & Business Media ISBN: 3540200401 Category : Computers Languages : en Pages : 395
Book Description
This first textbook on multi-relational data mining and inductive logic programming provides a complete overview of the field. It is self-contained and easily accessible for graduate students and practitioners of data mining and machine learning.
Author: Luc De Raedt Publisher: Morgan & Claypool Publishers ISBN: 1627058427 Category : Computers Languages : en Pages : 191
Book Description
An intelligent agent interacting with the real world will encounter individual people, courses, test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of these individuals and relations among them as well as cope with uncertainty. Uncertainty has been studied in probability theory and graphical models, and relations have been studied in logic, in particular in the predicate calculus and its extensions. This book examines the foundations of combining logic and probability into what are called relational probabilistic models. It introduces representations, inference, and learning techniques for probability, logic, and their combinations. The book focuses on two representations in detail: Markov logic networks, a relational extension of undirected graphical models and weighted first-order predicate calculus formula, and Problog, a probabilistic extension of logic programs that can also be viewed as a Turing-complete relational extension of Bayesian networks.
Author: Kristian Kersting Publisher: IOS Press ISBN: 9781586036744 Category : Computers Languages : en Pages : 258
Book Description
Talks about Logic Programming, Uncertainty Reasoning and Machine Learning. This book includes definitions that circumscribe the area formed by extending Inductive Logic Programming to cases annotated with probability values. It investigates the approach of Learning from proofs and the issue of upgrading Fisher Kernels to Relational Fisher Kernels.
Author: Luc De Raedt Publisher: Springer ISBN: 354078652X Category : Computers Languages : en Pages : 348
Book Description
This book provides an introduction to probabilistic inductive logic programming. It places emphasis on the methods based on logic programming principles and covers formalisms and systems, implementations and applications, as well as theory.
Author: Lise Getoor Publisher: MIT Press ISBN: 0262538687 Category : Computers Languages : en Pages : 602
Book Description
Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different proposed approaches, and applications. Handling inherent uncertainty and exploiting compositional structure are fundamental to understanding and designing large-scale systems. Statistical relational learning builds on ideas from probability theory and statistics to address uncertainty while incorporating tools from logic, databases and programming languages to represent structure. In Introduction to Statistical Relational Learning, leading researchers in this emerging area of machine learning describe current formalisms, models, and algorithms that enable effective and robust reasoning about richly structured systems and data. The early chapters provide tutorials for material used in later chapters, offering introductions to representation, inference and learning in graphical models, and logic. The book then describes object-oriented approaches, including probabilistic relational models, relational Markov networks, and probabilistic entity-relationship models as well as logic-based formalisms including Bayesian logic programs, Markov logic, and stochastic logic programs. Later chapters discuss such topics as probabilistic models with unknown objects, relational dependency networks, reinforcement learning in relational domains, and information extraction. By presenting a variety of approaches, the book highlights commonalities and clarifies important differences among proposed approaches and, along the way, identifies important representational and algorithmic issues. Numerous applications are provided throughout.
Author: Gerson Zaverucha Publisher: Springer Science & Business Media ISBN: 3540881891 Category : Computers Languages : en Pages : 304
Book Description
This book constitutes the refereed proceedings of the 19th Brazilian Symposium on Artificial Intelligence, SBIA 2008, held in Salvador, Brazil, in October 2008. The 27 revised full papers presented together with 3 invited lectures and 3 tutorials were carefully reviewed and selected from 142 submissions. The papers are organized in topical sections on computer vision and pattern recognition, distributed AI: autonomous agents, multi-agent systems and game knowledge representation and reasoning, machine learning and data mining, natural language processing, and robotics.
Author: G. Šír Publisher: IOS Press ISBN: 1643683438 Category : Computers Languages : en Pages : 239
Book Description
Deep learning has been used with great success in a number of diverse applications, ranging from image processing to game playing, and the fast progress of this learning paradigm has even been seen as paving the way towards general artificial intelligence. However, the current deep learning models are still principally limited in many ways. This book, ‘Deep Learning with Relational Logic Representations’, addresses the limited expressiveness of the common tensor-based learning representation used in standard deep learning, by generalizing it to relational representations based in mathematical logic. This is the natural formalism for the relational data omnipresent in the interlinked structures of the Internet and relational databases, as well as for the background knowledge often present in the form of relational rules and constraints. These are impossible to properly exploit with standard neural networks, but the book introduces a new declarative deep relational learning framework called Lifted Relational Neural Networks, which generalizes the standard deep learning models into the relational setting by means of a ‘lifting’ paradigm, known from Statistical Relational Learning. The author explains how this approach allows for effective end-to-end deep learning with relational data and knowledge, introduces several enhancements and optimizations to the framework, and demonstrates its expressiveness with various novel deep relational learning concepts, including efficient generalizations of popular contemporary models, such as Graph Neural Networks. Demonstrating the framework across various learning scenarios and benchmarks, including computational efficiency, the book will be of interest to all those interested in the theory and practice of advancing representations of modern deep learning architectures.
Author: Peter Flach Publisher: Wiley ISBN: 9780471942153 Category : Computers Languages : en Pages : 256
Book Description
An introduction to Prolog programming for artificial intelligence covering both basic and advanced AI material. A unique advantage to this work is the combination of AI, Prolog and Logic. Each technique is accompanied by a program implementing it. Seeks to simplify the basic concepts of logic programming. Contains exercises and authentic examples to help facilitate the understanding of difficult concepts.
Author: Claude Sammut Publisher: Springer Science & Business Media ISBN: 0387307680 Category : Computers Languages : en Pages : 1061
Book Description
This comprehensive encyclopedia, in A-Z format, provides easy access to relevant information for those seeking entry into any aspect within the broad field of Machine Learning. Most of the entries in this preeminent work include useful literature references.
Author: Luc De Raedt
Author: Luc De Raedt
Author: Luc De Raedt
Author: Luc De Raedt
Author: Kristian Kersting
Author: Luc De Raedt
Author: Lise Getoor
Author: Gerson Zaverucha
Author: G. Šír
Author: Peter Flach
Author: Claude Sammut