This is the first book to explain Ripple-Down Rules, an approach to building knowledge-based systems which is more similar to machine learning methods than other rule-based systems but which depends on using an expert rather than applying statistics to data The book provides detailed worked examples and uses publicly available software to demonstrate Ripple-Down Rules The examples enable users to build their own RDR tools
Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. - Algorithmic methods at the heart of successful data mining—including tried and true techniques as well as leading edge methods - Performance improvement techniques that work by transforming the input or output
This book suggests that classification is a key to human commonsense reasoning and transforms traditional considerations of data and knowledge communications, presenting an effective classification of logical rules used in the modeling of commonsense reasoning.
This book constitutes the refereed proceedings of the 12th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2000, held in Juan-les-Pins, France in October 2000. The 28 revised full papers and six revised short papers presented were carefully reviewed and selected from a high number of high-quality submissions. The book offers topical sections on knowledge modeling languages and tools, ontologies, knowledge acquisition from texts, machine learning, knowledge management and electronic commerce, problem solving methods, knowledge representation, validation, evaluation and certification, and methodologies.
Watching the environment and recognising patterns with the end goal of basic leadership is central to human instinct. This book manages the logical train that empowers comparable observation in machines through pattern recognition, which has application in differing innovation regions-character recognition, picture handling, modern computerization, web looks, discourse recognition, therapeutic diagnostics, target recognition, space science, remote detecting, information mining, biometric recognizable proof-to give some examples. This book is a composition of central subjects in pattern recognition utilizing an algorithmic approach. It gives a careful prologue to the ideas of pattern recognition and an efficient record of the real points in pattern recognition other than assessing the huge advance made in the field as of late. It incorporates fundamental strategies of pattern recognition, neural systems, bolster vector machines and choice trees. While hypothetical angles have been given due scope, the accentuation is more on the pragmatic. Pattern recognition has application in practically every field of human undertaking including topography, geology, space science and brain research. All the more particularly, it is helpful in bioinformatics, mental investigation, biometrics and a large group of different applications.
Organizational Learning and Knowledge: Concepts, Methodologies, Tools and Applications demonstrates exhaustively the many applications, issues, and techniques applied to the science of recording, categorizing, using and learning from the experiences and expertise acquired by the modern organization. A much needed collection, this multi-volume reference presents the theoretical foundations, research results, practical case studies, and future trends to both inform the decisions facing today's organizations and the establish fruitful organizational practices for the future. Practitioners, researchers, and academics involved in leading organizations of all types will find useful, grounded resources for navigating the ever-changing organizational landscape.
This book constitutes the refereed proceedings of the 14th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2004, held in Whittleburg Hall, UK in October 2004. The 30 revised full papers and 21 revised short papers were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on ontologies: mappings and translations; ontologies: problems and applications; ontologies: trust and e-learning; ontology maintenance; applications to medicine; portals; knowledge acquisition; Web services and problem solving; and searching, browsing, and knowledge acquisition.
The Machine Intelligence series was founded in 1965 by Donald Michie and has included many of the most important developments in the field over the past decades. This volume focuses on the theme of intelligent agents and features work by a number of eminent figures in artificial intelligence, including John McCarthy, Alan Robinson, Robert Kowalski, and Mike Genesereth. Topics include representations of consciousness, SoftBots, parallel implementations of logic, machine learning, machine vision, and machine-based scientific discovery in molecular biology.
This book constitutes the refereed proceedings of the 18th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2012, held in Galway City, Ireland, in October 2012. The 44 revised full papers were carefully reviewed and selected from 107 submissions. The papers are organized in topical sections on knowledge extraction and enrichment, natural language processing, linked data, ontology engineering and evaluation, social and cognitive aspects of knowledge representation, application of knowledge engineering, and demonstrations.
Intelligent assistant systems information is becoming the raw material of modern society. Access to information spaces and the capability to use them effectively and efficiently has become a key economical success factor. Intelligent Assistant Systems: Concepts, Techniques and Technologies contributes to the development of particular kinds of software and intelligent assistant systems, highlighting preliminary answers to the question, ?what is assistance?? Intelligent Assistant Systems: Concepts, Techniques and Technologies demonstrates that assistant systems will become reality, as the technology for implementing these systems is available and the problems that require assistance for their solutions are soon to be discovered. This book addresses intelligent assistant systems and issues, their scope, purpose, architecture, implementation, deployment, theoretical background, and use."
