Neural Networks: An Introductory Guide for Social Scientists

Neural Networks: An Introductory Guide for Social Scientists
ISBN-10
0857026275
ISBN-13
9780857026279
Series
Neural Networks
Category
Social Science
Pages
208
Language
English
Published
1998-09-24
Publisher
SAGE
Author
G David Garson

Description

This book provides the first accessible introduction to neural network analysis as a methodological strategy for social scientists. The author details numerous studies and examples which illustrate the advantages of neural network analysis over other quantitative and modelling methods in widespread use. Methods are presented in an accessible style for readers who do not have a background in computer science. The book provides a history of neural network methods, a substantial review of the literature, detailed applications, coverage of the most common alternative models and examples of two leading software packages for neural network analysis.

Other editions

Similar books

  • An Introduction to Neural Networks
    By Kevin Gurney

    As a comprehensive and highly accessible introduction to one of the most important topics in cognitive and computer science, this volume should interest a wide range of readers, both students and professionals, in cognitive science, ...

  • Neural Networks for Babies
    By Chris Ferrie

    It is never too early to become a scientist With scientific and mathematical information from an expert, this is the perfect book for enlightening the next generation of geniuses.

  • Neural Networks and Deep Learning: A Textbook
    By Charu C. Aggarwal

    This book covers both classical and modern models in deep learning.

  • Introduction to Neural Networks with Java
    By Jeff Heaton

    Introduction to Neural Networks in Java, Second Edition, introduces the Java programmer to the world of Neural Networks and Artificial Intelligence.

  • Exploring Neural Networks with C#
    By Nabendu Chaki, Ryszard Tadeusiewicz, Rituparna Chaki

    The utility of artificial neural network models lies in the fact that they can be used to infer functions from observations making them especially useful in applications where the complexity of data or tasks makes the design of such ...

  • Neural Networks with R: Smart models using CNN, RNN, deep learning, and artificial intelligence principles
    By Giuseppe Ciaburro, Balaji Venkateswaran

    About This Book Develop a strong background in neural networks with R, to implement them in your applications Build smart systems using the power of deep learning Real-world case studies to illustrate the power of neural network models Who ...

  • Fundamentals of Artificial Neural Networks
    By Mohamad H. Hassoun

    As book review editor of the IEEE Transactions on Neural Networks, Mohamad Hassoun has had the opportunity to assess the multitude of books on artificial neural networks that have appeared in recent years.

  • Neural Networks and Learning Machines
    By Simon S. Haykin

    Neural Networks and Learning Machines

  • Pattern Recognition and Neural Networks
    By Brian D. Ripley

    ... of the class Geos = { llow y [ cos ( w + x + b ) – cos ( b ) ] | 101 < rC ; } ( with pdf \ f ( w ) || CW || / C ) . ... Our class of approximating functions is the convex hull of G1 = { B ! ( a + b + x ) | IBI < 2rCf } and each&nbsp;...

  • Neural Networks and Learning Machines
    By Simon O. Haykin

    This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book.