ISBN: 0-471-45565-2
Hardcover
253 pages
May 2004
"The first introductory-level book to emphasize practical
applications through the use of example problems."
?? International Journal of General Systems, Vol. 31, No. 1, 2002,
on the first edition
The use of genetic algorithms (GAs) to solve large and often
complex computational problems has given rise to many new
applications in a variety of disciplines. Practical Genetic
Algorithms was the first introductory-level book on genetic
algorithms to emphasize practical applications rather than theory.
Practical Genetic Algorithms, Second Edition reflects the
significant evolution of the field since the book’s first
edition.
In an accessible style, the authors explain why the genetic
algorithm is superior in many real-world applications, cover
continuous parameter genetic algorithms, and provide in-depth
trade-off analysis of genetic algorithm parameter selection. This
Second Edition features:
Numerous practical example problems
A CD-ROM with MATLAB and High Performance Fortran codes
A new, more complete picture of traditional optimization
Revised examples reflecting recent research
Coverage of pareto-genetic and hybrid genetic algorithms (GAs)
New sections on hybrid GAs, parallel GAs, and messy GAs, with
recommendations on improving their performance
An all new chapter on simulated annealing, ant-colony
optimization, evolutionary strategies, and other cutting-edge
artificial intelligence methods of optimization
Written for the practicing scientist, engineer, economist,
artist, or anyone with an interest in the basics of GAs, the
second edition continues to offer readers an up-to-date look at
the evolving practical applications of GAs and how to manipulate
them in order to get the best performance.
ISBN: 0-471-21078-1
Hardcover
350 pages
June 2004
Description
A unified, coherent, and expansive treatment of current
classifier ensemble methods
Mail sorting, medical test reading, military target recognition,
signature verification, meteorological forecast, DNA matching,
fingerprint recognition. These are just a few of the areas
requiring reliable, precise pattern recognition.
Although in the past, pattern recognition has focused on
designing single classifiers, recently the focus has been on
combining several classifiers and getting a consensus of results
for greater accuracy. This interest in combining classifiers has
grown astronomically in recent years, evolving into a rich and
dynamic, if loosely structured, discipline. Combining Pattern
Classifiers: Methods and Algorithms represents the first attempt
to provide a comprehensive survey of this fast-growing field. In
a clear and straightforward manner, the author provides a much-needed
road map through a multifaceted and often controversial subject
while effectively organizing and systematizing the current state
of the art.
Covering a broad range of methodologies, algorithms, and
theories, the text addresses such questions as:
Why should we combine classifiers?
What are the current approaches for building classifier
ensembles?
What fusion methods can we use?
How do we measure diversity in a classifier ensemble and is
diversity really a key factor to its success?
Replete with case studies and real-world applications, this
groundbreaking text will be of interest to academics and
researchers in the field seeking both new classification tools
and new uses for the old ones.
ISBN: 0-471-65631-3
Hardcover
736 pages
August 2004
Description
This book is about the use of modern statistical methods for
quality control and improvement. It provides comprehensive
coverage of the subject from basic principles to state-of-art
concepts and applications. The objective is to give the reader a
sound understanding of the principles and the basis for applying
them in a variety of both product and nonproduct situations.
While statistical techniques are emphasized throughout, the book
has a strong engineering and management orientation. Guidelines
are given throughout the book for selecting the proper type of
statistical technique to use in a wide variety of product and
nonproduct situations. By presenting theory, and supporting the
theory with clear and relevant examples, Montgomery helps the
reader to understand the big picture of important concepts.
Updated to reflect contemporary practice and provide more
information on management aspects of quality improvement.
ISBN: 0-470-09095-2
Paperback
928 pages
September 2004
Description
The Concise Encyclopedia of Computer Science is the perfect desk
reference to guide you beyond the hype and jargon that entangle
computer technology and its ubiquitous applications.
Carefully edited to be readily accessible to the non-specialist,
this Concise Edition comprises shortened versions of over 60% of
the entries that appear in the full Fourth Edition of the
Encyclopedia of Computer Science (Ralston, Reilly and
Hemmendinger).
With many contributions by acknowledged experts, responsible for
the key developments that have shaped the discipline, articles
are classified within nine main themes:
Hardware
Computer Systems
Information and Data
Software
Mathematics of Computing
Theory of Computation
Methodologies
Applications
Computing Milieux
This structure enables you to follow a coherent pattern of study
or research in any given subfield.
Extensive cross-referencing at the start of every article
provides a clear roadmap to lead you to key related topics.
You'll also find appendices covering Abbreviations, Acronyms,
Notation and Units, a Timeline of Significant Milestones in
Computing, plus a full Index.
In short, all the back-up you'll need whether you are studying,
teaching or working in the field of computing
ISBN: 0-471-97647-4
Hardcover
400 pages
October 2004
Description
Research in the statistical analysis of extreme values has
flourished over the past decades: new probability models,
inference and data analysis techniques have been introduced,
while new application areas have been explored. Statistics of
Extremes: Theory and Applications covers a wide range of models
and applications, in particular in financial and actuarial risk
management, a major area of interest and relevance to extreme
value theory. Case studies are introduced in the first part of
the book and are used throughout to show the application of each
model discussed. The second part of the book covers advanced
topics, such as multivariate and Bayesian modelling of extremes.
Provides comprehensive coverage of a growing area of research.
Provides a good balance of theory and real-world applications.
Includes coverage of time series, regression, multivariate and
Bayesian modelling.
Introduces a number of case studies early in the book and
develops the theory around them.
Illustrated with many data examples and plots.
Statistics of Extremes: Theory and Applications will appeal to
researchers and graduate students of applied probability and
statistics, particularly those studying extreme values. It is
also suitable for applied statisticians working in finance and
insurance, pollution and climatology, geology, metallurgy, and
engineering.
ISBN: 0-471-21577-5
Hardcover
712 pages
August 2004
Description
Revised, updated, and even more useful to students, teachers, and
practicing professionals
The First Edition of Loss Models was deemed "worthy of
classical status" by the Journal of the International
Statistical Institute. While retaining its predecessor’s
thorough treatment of the concepts and methods of analyzing
contingent events, this powerful Second Edition is updated and
expanded to offer even more complete and flexible coverage of
risk theory, loss distributions, and survival models.
Beginning with a framework for model building and a description
of frequency and severity loss data typically available, it shows
readers how to combine frequency, severity, and loss models to
build aggregate loss models and credibility-based pricing models,
and how to analyze loss over multiple time periods. Important
features of this new edition include:
Thorough preparation for relevant parts of preliminary
examinations of the Society of Actuaries (SOA) and Casualty
Actuarial Society (CAS)
Exercises based on past SOA and CAS exams
Examples using actual insurance data
Practical treatment of modern credibility theory
Data files and more from an ftp site
Loss Models, Second Edition is an important resource, providing a
comprehensive, practically motivated toolkit and an excellent
reference, for actuaries preparing for SOA and CAS preliminary
examinations, students in actuarial science who need to
understand loss and risk models, and practicing professionals
involved in loss modeling.