Author : Larry Bull
Publisher : Springer Science & Business Media
Page : 234 pages
File Size : 14,36 MB
Release : 2008-05-29
Category : Computers
ISBN : 3540789782
The ability of Learning Classifier Systems (LCS) to solve complex real-world problems is becoming clear. This book brings together work by a number of individuals who demonstrate the good performance of LCS in a variety of domains.
Author : Xin-She Yang
Publisher : Academic Press
Page : 190 pages
File Size : 37,40 MB
Release : 2019-06-17
Category : Mathematics
ISBN : 0128172177
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. Presents an informal, theorem-free approach with concise, compact coverage of all fundamental topics Includes worked examples that help users increase confidence in their understanding of key algorithms, thus encouraging self-study Provides algorithms and techniques that can be implemented in any programming language, with each chapter including notes about relevant software packages
Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 779 pages
File Size : 17,12 MB
Release : 2020-01-30
Category : Business & Economics
ISBN : 1108473989
New to the second edition of this advanced text are several chapters on regression, including neural networks and deep learning.
Author : Ian H. Witten
Publisher : Elsevier
Page : 665 pages
File Size : 12,82 MB
Release : 2011-02-03
Category : Computers
ISBN : 0080890369
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. - Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects - Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods - Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization
Author : Tim Kovacs
Publisher : Springer
Page : 356 pages
File Size : 11,55 MB
Release : 2007-06-11
Category : Computers
ISBN : 3540712313
This book constitutes the thoroughly refereed joint post-proceedings of three consecutive International Workshops on Learning Classifier Systems that took place in Chicago, IL in July 2003, in Seattle, WA in June 2004, and in Washington, DC in June 2005. Topics in the 22 revised full papers range from theoretical analysis of mechanisms to practical consideration for successful application of such techniques to everyday datamining tasks.
Author : Pier L. Lanzi
Publisher : Springer Science & Business Media
Page : 232 pages
File Size : 10,88 MB
Release : 2002-06-12
Category : Computers
ISBN : 3540437932
Thechapterinvestigateshowmodelandbehaviorallearning can be improved in an anticipatory learning classi?er system by bi- ing exploration. First, theappliedsystemACS2isexplained. Next,an overviewoverthepossibilitiesofapplyingexplorationbiasesinanant- ipatory learning classi?er systemand speci?cally ACS2 is provided.
Author : Xanthoula-Eirini Pantazi
Publisher : Academic Press
Page : 334 pages
File Size : 18,11 MB
Release : 2019-10-08
Category : Business & Economics
ISBN : 0128143924
Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. - Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture - Addresses AI use in weed management, disease detection, yield prediction and crop production - Utilizes case studies to provide real-world insights and direction
Author : Mohammed J. Zaki
Publisher : Cambridge University Press
Page : 607 pages
File Size : 33,72 MB
Release : 2014-05-12
Category : Computers
ISBN : 0521766338
A comprehensive overview of data mining from an algorithmic perspective, integrating related concepts from machine learning and statistics.
Author : Larry Bull
Publisher : Springer
Page : 309 pages
File Size : 13,56 MB
Release : 2012-08-13
Category : Computers
ISBN : 3540399259
The field called Learning Classifier Systems is populated with romantics. Why shouldn't it be possible for computer programs to adapt, learn, and develop while interacting with their environments? In particular, why not systems that, like organic populations, contain competing, perhaps cooperating, entities evolving together? John Holland was one of the earliest scientists with this vision, at a time when so-called artificial intelligence was in its infancy and mainly concerned with preprogrammed systems that didn't learn. that, like organisms, had sensors, took Instead, Holland envisaged systems actions, and had rich self-generated internal structure and processing. In so doing he foresaw and his work prefigured such present day domains as reinforcement learning and embedded agents that are now displacing the older "standard Af' . One focus was what Holland called "classifier systems": sets of competing rule like "classifiers", each a hypothesis as to how best to react to some aspect of the environment--or to another rule. The system embracing such a rule "popu lation" would explore its available actions and responses, rewarding and rating the active rules accordingly. Then "good" classifiers would be selected and re produced, mutated and even crossed, a la Darwin and genetics, steadily and reliably increasing the system's ability to cope.
Author : Pier L. Lanzi
Publisher : Springer
Page : 344 pages
File Size : 10,53 MB
Release : 2003-06-26
Category : Computers
ISBN : 3540450270
Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.
