By Robert A. Dunne

ISBN-10: 0470148144

ISBN-13: 9780470148143

ISBN-10: 0471741086

ISBN-13: 9780471741084

An obtainable and updated remedy that includes the relationship among neural networks and information

A Statistical method of Neural Networks for trend acceptance offers a statistical remedy of the Multilayer Perceptron (MLP), that's the main commonly used of the neural community types. This e-book goals to reply to questions that come up while statisticians are first faced with this kind of version, reminiscent of:

How strong is the version to outliers?

may well the version be made extra strong?

Which issues could have a excessive leverage?

What are stable beginning values for the perfect set of rules?

Thorough solutions to those questions and plenty of extra are integrated, in addition to labored examples and chosen difficulties for the reader. Discussions at the use of MLP versions with spatial and spectral information also are integrated. extra therapy of hugely very important critical features of the MLP are supplied, similar to the robustness of the version within the occasion of outlying or unusual information; the effect and sensitivity curves of the MLP; why the MLP is a reasonably powerful version; and alterations to make the MLP extra strong. the writer additionally presents explanation of a number of misconceptions which are standard in current neural community literature.

in the course of the booklet, the MLP version is prolonged in different instructions to teach statistical modeling method could make helpful contributions, and additional exploration for becoming MLP types is made attainable through the R and S-PLUS® codes which are on hand at the book's comparable site. A Statistical method of Neural Networks for trend acceptance effectively connects logistic regression and linear discriminant research, therefore making it a serious reference and self-study consultant for college students and pros alike within the fields of arithmetic, statistics, computing device technology, and electric engineering.

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**Extra info for A statistical approach to neural networks for pattern recognition**

**Example text**

S. Air Force to represent the functions and subroutines of complex computer software systems. Douglas Ross, the original developer of this method, has also referred to it as SADT, an acronym for Software Analysis and Design Technique (Ross 1977). The original Air Force report describing IDEFO is out of print, but an edited version was republished by Mayer (1992). IDEFO diagrams were developed for software systems, but they provide a powerful graphical means for describing any kind of system that is the subject of a scientific investigation.

The label above each column of plots gives the second independent variable, which changes value between the two curves in each plot in that column. Main effect plots occur on the diagonal of this matrix of plots, and have only a single curve. Plots of i vs. j and j vs. Xj and xcX/ terms (curvature changes) in addition to XcXj terms (slope changes), if they are present. Xj term, while the plot of factor j for two levels of i is needed to check for a Xjx/ term. The XiXj term can be identified from either of these two plots, and so a consistency check can be made for this term.

Draw cause-effect diagrams for each dependent variable for the experiment. Identify and classify all important variables from these diagrams. Discuss the differences in using this approach compared to the IDEFO approach. Which variables were easier to identify? To classify? Which were more difficult? 52 2.

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