o f t i t l e s , v i s i t u s a t w w w. c r c p r e s s . c o m Multi-Agent Systems Textbook
Edited by
Machine Learning
Adelinde M. Uhrmacher
An Algorithmic Perspective
University of Rostock, Germany
Danny Weyns
Stephen Marsland
Katholieke Universiteit Leuven, Belgium
Massey University, Palmerston North, New Zealand
Series: Computational Analysis, Synthesis, and Design of Dynamic Systems
Introduction to Machine Learning introduces this subject to computer science students and others who may not have a strong mathematical background. Focusing on algorithms and applications, the text presents three distinct sets of problems for each section: standard questions that test understanding of the material, structured programming exercises using code and data from the Internet, and suggested further investigations, often involving some basic programming. The book covers such fundamental topics as neuronal modeling, perceptron, multi-layer perceptron, classification, regression, decision trees, the naïve Bayes’ classifier, unsupervised learning, the self-organizing map, and genetic algorithms.
Multiple agent systems (MAS) have been used extensively as a tool for designing simulation problems; while simulation has often been used for the design of multiple agent studies. This book focuses on the intersection of MAS and simulation, emphasizing application domains. The book starts with a historical overview from different perspectives. A second part discusses the use of simulation in MAS and among a wealth of topics, explains simulation support for agent decision making. A third section zooms in on the specifics of MAS for simulation and a last section presents a number of representative platforms and tools for MAS and simulation.
Catalog no. C6718, ISBN: 978-1-4200-6718-7 April 2009, 6-1/8 x 9-1/4, 300 pp. Suggested Price: $59.95 / £36.99
Machine Learning Algorithms for Spatial Data Analysis and Modelling
Catalog no. 70231, ISBN: 978-1-4200-7023-1 January 2009, 7 x 10, 536 pp. Suggested Price: $139.95 / £85.00
Semisupervised Learning for Computational Linguistics
67
Steven Abney
Mikhail Kanevski, Vadim Timonin, and Alexi Pozdnukhov
University of Michigan, Ann Arbor, USA
This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data, and they also include case studies based on environmental and pollution data. This comprehensive and practical volume includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.
This book provides a broad, accessible treatment of the theory and linguistic applications of semisupervised methods. It presents a brief history of the field before moving on to discuss well-known natural language processing methods, such as self-training and cotraining. It then centers on machine learning techniques, including the boundary-oriented methods of perceptrons, boosting, SVMs, and the null-category noise model. In addition, the book covers clustering, the EM algorithm, related generative methods, and agreement methods. It concludes with the graph-based method of label propagation along with a detailed discussion of spectral methods.
University of Lausanne, Switzerland
Catalog no. EF8237, ISBN: 978-0-8493-8237-6 January 2009, 6 x 9, 400 pp. Suggested Price: $109.95 / £66.99
Machine Learning & Pattern Recognition
Simulation and Applications
Series: Chapman & Hall/CRC Computer Science & Data Analysis
Catalog no. C5599, ISBN: 978-1-58488-559-7 2008, 6-1/8 x 9-1/4, 320 pp. Suggested Price: $83.95 / £46.99
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