Sensitivity Analysis for Neural Networks

By: Daniel S. Yeung, Ian Cloete, Daming Shi

Write A Review

Hardcover | 18 November 2009

At a Glance

Hardcover
96 Pages

Dimensions(cm)
23.5 x 15.88 x 0.64

Hardcover

$169.00

or 4 interest-free payments of $42.25 with

Ships in 5 to 7 business days

Artificial neural networks are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering systems, and sensitivity analysis concerns methods for analyzing these relationships. Perturbations of neural networks are caused by machine imprecision, and they can be simulated by embedding disturbances in the original inputs or connection weights, allowing us to study the characteristics of a function under small perturbations of its parameters.

This is the first book to present a systematic description of sensitivity analysis methods for artificial neural networks. It covers sensitivity analysis of multilayer perceptron neural networks and radial basis function neural networks, two widely used models in the machine learning field. The authors examine the applications of such analysis in tasks such as feature selection, sample reduction, and network optimization. The book will be useful for engineers applying neural network sensitivity analysis to solve practical problems, and for researchers interested in foundational problems in neural networks.

Industry Reviews

From the reviews:

"Neural Networks are seen as an information paradigm inspired by the way the human brain processes information. ... The book may be used by researchers in diverse domains, such as neural networks, machine learning, computer engineering, etc., facing problems connected to sensitivity analysis of neural networks." (Florin Gorunescu, Zentralblatt MATH, Vol. 1189, 2010)

Shipping

	Standard Shipping	Express Shipping
Metro postcodes:	$9.99	$14.95
Regional postcodes:	$9.99	$14.95
Rural postcodes:	$9.99	$14.95

Orders over $79.00 qualify for free shipping.

How to return your order

At Booktopia, we offer hassle-free returns in accordance with our returns policy. If you wish to return an item, please get in touch with Booktopia Customer Care.

Additional postage charges may be applicable.

Defective items

If there is a problem with any of the items received for your order then the Booktopia Customer Care team is ready to assist you.

For more info please visit our Help Centre.

You Can Find This Book In

Non-Fiction Mathematics Computing & I.T.Computer Science Artificial Intelligence Neural Networks & Fuzzy Systems Graphical & Digital Media Applications 3D Graphics & Modelling Computer Vision Engineering & Technology Technology in General Technical Design

Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering Robotics Science Physics

Introduction to Neural Networks	p. 1
Properties of Neural Networks	p. 3
Neural Network Learning	p. 5
Supervised Learning	p. 5
Unsupervised Learning	p. 5
Perceptron	p. 6
Adaline and Least Mean Square Algorithm	p. 8
Multilayer Perceptron and Backpropagation Algorithm	p. 9
Output Layer Learning	p. 11
Hidden Layer Learning	p. 11
Radial Basis Function Networks	p. 12
Support Vector Machines	p. 13
Principles of Sensitivity Analysis	p. 17
Perturbations in Neural Networks	p. 17
Neural Network Sensitivity Analysis	p. 18
Fundamental Methods of Sensitivity Analysis	p. 21
Geometrical Approach	p. 21
Statistical Approach	p. 23
Summary	p. 24
Hyper-Rectangle Model	p. 25
Hyper-Rectangle Model for Input Space of MLP	p. 25
Sensitivity Measure of MLP	p. 26
Discussion	p. 27
Sensitivity Analysis with Parameterized Activation Function	p. 29
Parameterized Antisymmetric Squashing Function	p. 29
Sensitivity Measure	p. 30
Summary	p. 31
Localized Generalization Error Model	p. 33
Introduction	p. 33
The Localized Generalization Error Model	p. 35
The Q-Neighborhood and Q-Union	p. 36
The Localized Generalization Error Bound	p. 36
Stochastic Sensitivity Measure for RBFNN	p. 38
Characteristics of the Error Bound	p. 40
Comparing Two Classifiers Using the Error Bound	p. 42
Architecture Selection Using the Error Bound	p. 42
Parameters for MC²SG	p. 44
RBFNN Architecture Selection Algorithm for MC²SG	p. 44
A Heuristic Method to Reduce the Computational Time for MC²SG	p. 45
Summary	p. 45
Critical Vector Learning for RBF Networks	p. 47
Related Work	p. 47
Construction of RBF Networks with Sensitivity Analysis	p. 48
RBF Classifiers' Sensitivity to the Kernel Function Centers	p. 49
Orthogonal Least Square Transform	p. 51
Critical Vector Selection	p. 52
Summary	p. 52
Sensitivity Analysis of Prior Knowledge	p. 55
KBANNs	p. 56
Inductive Bias	p. 58
Sensitivity Analysis and Measures	p. 59
Output-Pattern Sensitivity	p. 59
Output-Weight Sensitivity	p. 60
Output-H Sensitivity	p. 61
Euclidean Distance	p. 61
Promoter Recognition	p. 61
Data and Initial Domain Theory	p. 62
Experimental Methodology	p. 63
Discussion and Conclusion	p. 64
Applications	p. 69
Input Dimension Reduction	p. 69
Sensitivity Matrix	p. 70
Criteria for Pruning Inputs	p. 70
Network Optimization	p. 71
Selective Learning	p. 74
Hardware Robustness	p. 76
Measure of Nonlinearity	p. 77
Parameter Tuning for Neocognitron	p. 78
Receptive Field	p. 79
Selectivity	p. 80
Sensitivity Analysis of the Neocognitron	p. 80
Bibliography	p. 83
Table of Contents provided by Ingram. All Rights Reserved.

Sensitivity Analysis for Neural Networks

At a Glance

Hardcover

Industry Reviews

Shipping

How to return your order

Defective items

You Can Find This Book In

More in Computer Science

Python Crash Course

3rd Edition - A Hands-On, Project-Based Introduction to Programming

Microsoft 365 Excel All-in-One For Dummies

Excel for Dummies

The New Age of Sexism

How the AI Revolution is Reinventing Misogyny

The AI Con

How To Fight Big Tech's Hype and Create the Future We Want

Microsoft 365 Excel For Dummies

For Dummies (Computer/Tech)

If Anyone Builds It, Everyone Dies

The Case Against Superintelligent AI

Nexus

A Brief History of Information Networks from the Stone Age to AI

Creative Machines

AI, Art & Us

The AI-Driven Leader

Harnessing AI to Make Faster, Smarter Decisions

Genesis

Artificial Intelligence, Hope, and the Human Spirit

The Tech Coup

How to Save Democracy from Silicon Valley

The Coming Wave

AI, Power and the 21st Century's Greatest Dilemma

New Beginnings

why change is so difficult and how to achieve it

The Shortest History of AI

Building Machine Learning Systems with a Feature Store

Batch, Real-Time, and LLM Systems

Python Workout, Second Edition

200 ten-minute exercises

Life 3.0

Being Human in the Age of Artificial Intelligence

Python All-in-One For Dummies

3rd Edition

Artificial Intelligence

A Modern Approach, 4th Global Edition

Falter

Has the Human Game Begun to Play Itself Out?

Storytelling with Data

A Data Visualization Guide for Business Professionals, 10th Anniversary Edition

Effective Modern C++

42 Specific Ways to Improve Your Use of C++11 And C++14

Scary Smart

The Future of Artificial Intelligence and How You Can Save Our World

Fundamentals of Software Architecture

A Modern Engineering Approach

This product is categorised by