Home | Amazing | Today | Tags | Publishers | Years | Account | Search 
Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More (Texts in Computer Science)

Buy
This book was originally titled “Fundamentals of the New Artificial Intelligence: Beyond Traditional Paradigms.” I have changed the subtitle to better represent the contents of the book. The basic philosophy of the original version has been kept in the new edition. That is, the book covers the most essential and widely employed material in each area, particularly the material important for real-world applications. Our goal is not to cover every latest progress in the fields, nor to discuss every detail of various techniques that have been developed. New sections/subsections added in this edition are: Simulated Annealing (Section 3.7), Boltzmann Machines (Section 3.8) and Extended Fuzzy if-then Rules Tables (Sub-section 5.5.3). Also, numerous changes and typographical corrections have been made throughout the manuscript. The Preface to the first edition follows.

General scope of the book

Artificial intelligence (AI) as a field has undergone rapid growth in diversification and practicality. For the past few decades, the repertoire of AI techniques has evolved and expanded. Scores of newer fields have been added to the traditional symbolic AI. Symbolic AI covers areas such as knowledge-based systems, logical reasoning, symbolic machine learning, search techniques, and natural language processing. The newer fields include neural networks, genetic algorithms or evolutionary computing, fuzzy systems, rough set theory, and chaotic systems. The traditional symbolic AI has been taught as the standard AI course, and there are many books that deal with this aspect. The topics in the newer areas are often taught individually as special courses, that is, one course for neural networks, another course for fuzzy systems, and so on. Given the importance of these fields together with the time constraints in most undergraduate and graduate computer science curricula, a single book covering the areas at an advanced level is desirable. This book is an answer to that need.
(HTML tags aren't allowed.)

Probability and Statistics for Computer Scientists
Probability and Statistics for Computer Scientists

Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic
...

Coding Games in Scratch: A Step-by-Step Visual Guide to Building Your Own Computer Games
Coding Games in Scratch: A Step-by-Step Visual Guide to Building Your Own Computer Games

Written for children ages 8–12 with little to no coding experience, this straightforward visual guide uses fun graphics and easy-to-follow instructions to show young learners how to build their own computer projects using Scratch, a popular free programming language.

With Coding Games in Scratch, kids can build single...

JAVA DESIGN PATTERNS
JAVA DESIGN PATTERNS
If a problem occurs over and over again, a solution to that problem has been used effectively. That solution is described as a pattern. The design patterns are languageindependent strategies for solving common object-oriented design problems. When you make a design, you should know the names of some common solutions. Learning design...

Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)
Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world problems with data. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the...

Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists

Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you’ll learn techniques for extracting and transforming features—the numeric representations of raw data—into formats for machine-learning models. Each chapter guides you...

Computer Science Distilled: Learn the Art of Solving Computational Problems
Computer Science Distilled: Learn the Art of Solving Computational Problems

A walkthrough of computer science concepts you must know. Designed for readers who don't care for academic formalities, it's a fast and easy computer science guide. It teaches the foundations you need to program computers effectively. After a simple introduction to discrete math, it presents common algorithms and data structures. It...

©2018 LearnIT (support@pdfchm.net) - Privacy Policy