Logical and Relational Learning

Author: Luc De Raedt
Publisher: Springer Science & Business Media
ISBN: 3540688560
Format: PDF, Docs
Download Now
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.

Foundations of Rule Learning

Author: Johannes Fürnkranz
Publisher: Springer Science & Business Media
ISBN: 3540751971
Format: PDF, ePub, Mobi
Download Now
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.

Inductive Logic Programming

Author: Katsumi Inoue
Publisher: Springer
ISBN: 3319405667
Format: PDF, ePub, Mobi
Download Now
This book constitutes the thoroughly refereed post-conference proceedings of the 25th International Conference on Inductive Logic Programming, ILP 2015, held in Kyoto, Japan, in August 2015. The 14 revised papers presented were carefully reviewed and selected from 44 submissions. The papers focus on topics such as theories, algorithms, representations and languages, systems and applications of ILP, and cover all areas of learning in logic, relational learning, relational data mining, statistical relational learning, multi-relational data mining, relational reinforcement learning, graph mining, connections with other learning paradigms, among others.

Perspectives on Ontology Learning

Author: J. Lehmann
Publisher: IOS Press
ISBN: 1614993793
Format: PDF, ePub
Download Now
Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-start in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.

Neural Symbolic Cognitive Reasoning

Author: Artur S. D'Avila Garcez
Publisher: Springer Science & Business Media
ISBN: 3540732454
Format: PDF, Mobi
Download Now
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.

Dynamic Fuzzy Machine Learning

Author: Fanzhang Li
Publisher: Walter de Gruyter GmbH & Co KG
ISBN: 3110520656
Format: PDF, ePub
Download Now
Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field of big data, cloud computing, the Internet of Things, and quantum information. At present, the processing of this kind of data is not very mature. The authors carried out more than 20 years of research, and show in this book their most important results. The seven chapters of the book are devoted to key topics such as dynamic fuzzy machine learning models, dynamic fuzzy self-learning subspace algorithms, fuzzy decision tree learning, dynamic concepts based on dynamic fuzzy sets, semi-supervised multi-task learning based on dynamic fuzzy data, dynamic fuzzy hierarchy learning, examination of multi-agent learning model based on dynamic fuzzy logic. This book can be used as a reference book for senior college students and graduate students as well as college teachers and scientific and technical personnel involved in computer science, artificial intelligence, machine learning, automation, data analysis, mathematics, management, cognitive science, and finance. It can be also used as the basis for teaching the principles of dynamic fuzzy learning.

Introduction to Statistical Relational Learning

Author: Lise Getoor
Publisher: MIT Press
ISBN: 0262072882
Format: PDF, ePub, Mobi
Download Now
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.

Markov Logic

Author: Pedro Domingos
Publisher: Morgan & Claypool Publishers
ISBN: 1598296922
Format: PDF, ePub, Mobi
Download Now
Most subfields of computer science have an interface layer via which applications communicate with the infrastructure, and this is key to their success (e.g., the Internet in networking, the relational model in databases, etc.). So far this interface layer has been missing in AI. First-order logic and probabilistic graphical models each have some of the necessary features, but a viable interface layer requires combining both. Markov logic is a powerful new language that accomplishes this by attaching weights to first-order formulas and treating them as templates for features of Markov random fields. Most statistical models in wide use are special cases of Markov logic, and first-order logic is its infinite-weight limit. Inference algorithms for Markov logic combine ideas from satisfiability, Markov chain Monte Carlo, belief propagation, and resolution. Learning algorithms make use of conditional likelihood, convex optimization, and inductive logic programming. Markov logic has been successfully applied to problems in information extraction and integration, natural language processing, robot mapping, social networks, computational biology, and others, and is the basis of the open-source Alchemy system. Table of Contents: Introduction / Markov Logic / Inference / Learning / Extensions / Applications / Conclusion

Neural Symbolic Learning Systems

Author: Artur S. d'Avila Garcez
Publisher: Springer Science & Business Media
ISBN: 1447102118
Format: PDF, Kindle
Download Now
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.