Object-Oriented Neural Networks in C++ [Paperback]

Joey Rogers (Author)

Book Description

ISBN-10: 0125931158 | ISBN-13: 978-0125931151 | Publication Date: October 29, 1996 | Edition: 1

"This book is distinctive in that it implements nodes and links as base objects and then composes them into four different kinds of neural networks. Roger's writing is clear....The text and code are both quite readable. Overall, this book will be useful to anyone who wants to implement neural networks in C++ (and, to a lesser extent, in other object-oriented programming languages.)...I recommend this book to anyone who wants to implement neural networks in C++."--D.L. Chester, Newark, Delaware in COMPUTING REVIEWSObject-Oriented Neural Networks in C++ is a valuable tool for anyone who wants to understand, implement, or utilize neural networks. This book/disk package provides the reader with a foundation from which any neural network architecture can beconstructed. The author has employed object-oriented design and object-oriented programming concepts to develop a set of foundation neural network classes, and shows how these classes can be used to implement a variety of neural network architectures with a great deal of ease and flexibility. A wealth of neural network formulas (with standardized notation), object code implementations, and examples are provided to demonstrate the object-oriented approach to neural network architectures and to facilitatethe development of new neural network architectures. This is the first book to take full advantage of the reusable nature of neural network classes.

Key Features
* Describes how to use the classes provided to implement a variety of neural network architectures including ADALINE, Backpropagation, Self-Organizing, and BAM
* Provides a set of reusable neural network classes, created in C++, capable of implementing any neural network architecture
* Includes an IBM disk of the source code for the classes, which is platform independent
* Includes an IBM disk with C++ programs described in the book

Editorial Reviews

Review

"This book is distinctive in that it implements nodes and links as base objects and then composes them into four different kinds of neural networks. Rogers writing is clear....The text and code are both quite readable. Overall, this book will be useful to anyone who wants to implement neural networks in C++ (and, to a lesser extent, in other object-oriented programming languages.)...I recommend this book to anyone who wants to implement neural networks in C++."
--D.L. Chester, Newark, Delaware in COMPUTING REVIEWS

From the Back Cover

A wealth of neural network formulas (with standardized notation), object code implementation, and examples are provided to demonstrate the object-oriented approach to neural network architectures. Describes how to use the provided classes to implement a variety of neural network architectures, including backpropagation neural network, BAM, Adaline, and self-organizing neural network. A set of reusable neural network classes capable of implementing any neural network architecture is provided. The included floppy disk contains the source code.

-- Copyright © 1999 Book News, Inc., Portland, OR All rights reserved

This book/disk package provides the reader with a foundation from which any neural network
architecture can be constructed. The author has employed object-oriented design and
object-oriented programming concepts to develop a set of foundation neural network classes,
and shows how these classes can be used to implement a variety of neural network architecture with a great deal of ease and flexibility.

See all Editorial Reviews

Product Details

• Paperback: 310 pages
• Publisher: Morgan Kaufmann; 1 edition (October 29, 1996)
• Language: English
• ISBN-10: 0125931158
• ISBN-13: 978-0125931151
• Product Dimensions: 8.9 x 7.5 x 0.6 inches
• Shipping Weight: 1.2 pounds
• Average Customer Review: 3.8 out of 5 stars  See all reviews (21 customer reviews)
• Amazon Best Sellers Rank: #1,236,706 in Books (See Top 100 in Books)

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Customer Reviews

Most Helpful Customer Reviews

14 of 14 people found the following review helpful:

4.0 out of 5 stars A great beginner's book on Neural Networks., August 12, 1998

By A Customer

This review is from: Object-Oriented Neural Networks in C++ (Paperback)

This book is an excellent book for people with little or no background in Neural Networks. The description and name of the book is pretty much what you'll get. The book begins by giving a background in C++, from there they build the basic objects and theory needed for neural nets in general. Then Rogers proceeds to give a quick overview of each of the four neural nets that he covers in the book, in terms of mathematical definitions of those networks. Then the next chapters are dedicated to his implementation of each one of those four networks (Adaline, Backpropagation, Self-Organizing, & Binary Access Memory), including examples of how each one could be used.

If you are looking on a book that goes into a large variety of neural networks, or that goes into the in-depth theory behind neural networks, then this might not be the book that you are looking for. Although Rogers gives a sufficient background and a firm basis of neural nets and C++ (you shouldn't get lost in the reading), his main purpose in the book is the actual programming implementation. The books main function (in my view), is to provide the reader with the tools on how to actually approach PROGRAMMING neural networks.

My only complaint for this book was the extent of the examples that Rogers provided. The examples were limited in scope (and he mentions this) and detail. It would have been a little more helpful if more "application" information had been given; instead of just "programming" information. Still, though, the book is worth it if you want a strong basis on coding a neural network.

All in all a great book, if you take into consideration the book's purpose. Rogers has a clean and clear writing style. He provides enough fundamental information throughout the book in terms of programming background and neural network background before proceeding to give the "meat" of the book.

Christopher Sean Morrison

17 of 18 people found the following review helpful:

5.0 out of 5 stars A very good book for those begining with Neural Networks., July 8, 1997

By A Customer

This review is from: Object-Oriented Neural Networks in C++ (Paperback)

Unlike many books on similar topics, Object-Oriented Neural-Networks in C++ provides a truly object-oriented approach to the design and implementation of neural networks. This book is not only a good tool for those beginning with neural-networks giving step-by-step instructions for constructing a variety of neural network architectures, it also provides a good set of tools for the advanced neural network user to build more flexible and powerful neural-network architectures. The book covers 4 basic neural network architectures (ADALINE, Backpropagation, Self-Organizing, and BAM); however, the object foundation provided by the book can be the basis for any neural network architecture. The book contains a complete set of source code (in print and on disk) for all objects described, real-world examples, and a review of the C++ programming techniques used throughout the book

32 of 39 people found the following review helpful:

2.0 out of 5 stars Bad code. Bad book., June 28, 2001

By 

"jdwilder" (Berkeley, CA United States) - See all my reviews

This review is from: Object-Oriented Neural Networks in C++ (Paperback)

If you're trying to learn neural nets, this book gives you a fairly decent overview of four different types. It doesn't go into much depth, but it's easy for a beginner to pick up. If you're trying learn object-oriented programming and/or c++, _don't_buy_this_book_. The code is buggy, bloated, poorly designed, inefficient and just plain bad. To list some of the problems I encountered with it:

___________________________________________________________________________________________________________________________________________________________________________________________________ 1) The Base_Link::Save function corrupts object and writes corrupted data to a file. 2) No const, *anywhere*. 3) Repeatedly attempts to inline virtual functions. Virtual functions *can't* be inlined because they aren't evaluated until runtime. 4) Most function implementations placed in header files. Maybe due to the use of those magical "inline virtual" functions. 5) Never uses STL containers even where they would be optimal. Creates arrays with new where std::vector would work better, and defines custom linked-list class (very ugly). 6) Uses ofstream/ifstream where ostream/istream would be preferable. The whole point of c++ streams is to have a single set of interfaces for handling io from any origin. Using the fstreams just creates an unnecessary restriction. 7) Creates an array in base class interfaces for use by subclasses as a variable container, as opposed to just creating variables in the subclasses. In my opinion, this is his most egregious offense. It defeats the whole purpose of inheritance. 8) Use of static data in classes. Don't bother using this code in a multithreaded app. Neural nets are usually well suited for multithreading, but certainly not this implementation. 9) Non-virtual destructor in interface classes. Doesn't matter in this case, because he stored all implementation-specific data in the interface class, but is generally poor practice because it can lead to memory leaks. 10) Too few comments that aren't completely redundant, for example: "Base_Link(); // Constructor" 11) The classes are unnecessarily general. Just about every function is virtual, even ones that should never be overridden. The inheritance heirarchy of his objects also suggests that nodes and links can vary implementation within a single network, but never do. In fact, link, node, and neural-network (link/node container) objects all make explicit assumptions about each others' implementations. The examples will crash if you try to use objects of varying implementations together. 12) There are functions in the interface classes that vary *usage* depending on implementation. This means you cannot use them without knowing the exact implementation you are dealing with. The whole point of virtual functions is to provide a single function with a consistent usage even though the implementation may vary. ___________________________________________________________________________________________________________________________________________________________________________________________________ To sum up, if you want a simple overview of a few neural network types and love a fixer-upper, this is the book for you. Otherwise don't bother.