The second edition adds new language features and end-of-chapter GUI sections that include animation. New chapters include an introduction to the Java Collections Framework and an in-depth treatment of recursion. Two new supplementary chapters on the bookâs companion website describe the JavaFX GUI platform.
Before diving into object-oriented programming (OOP) in Chapter 6, the second edition includes a âmini-chapterâ that describes how to write multiple-method programs in a non-OOP environment. Those who want to continue this theme can follow an optional âlate objectsâ approach by reading two chapters on the bookâs website before returning to OOP in Chapter 6. Some key features include:
â¢A conversational, easy-to-follow writing style.
â¢Simple GUI programming early, in an optional standalone graphics track.
â¢Well-identified alternatives for altering the book's sequence to fit individual needs.
â¢Well-developed projects in six different academic disciplines, with a handy summary.
â¢Detailed customizable PowerPointTM lecture slides, with icon-keyed hidden notes.
I have used the Dean and Dean book in my Introduction to Java Programming class for the past year. This is an excellent text and I am very happy with it. It is the only text that I have ever used that always gets positive comments from students on my class evaluations even though there is no question asked about the text. The chapters are well thought out and the coverage is complete. The progression from topic-to-topic is masterful, and the writing is exceptionally clear and at the perfect level for an introductory Java class.
â Ralph Duffy, South Seattle Community College
Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition.
It includes Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition.
This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision.
Genetic algorithms have been used in science and engineering as adaptive algorithms for solving practical problems and as computational models of natural evolutionary systems. This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting "general purpose" nature of genetic algorithms as search methods that can be employed across disciplines.
An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.
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