From the Publisher:
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modelling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.

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Neural networks for pattern recognition

This paper describes a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction. It also establishes consistency of the estimated covariance matrix under fairly general conditions.

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A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix

Structural equation modeling (SEM) has become a quasi-standard in marketing and management research when it comes to analyzing the cause-effect relations between latent constructs. For most researchers, SEM is equivalent to carrying out covariance-based SEM (CB-SEM). While marketing researchers have a basic understanding of CB-SEM, most of them are only barely familiar with the other useful approach to SEM-partial least squares SEM (PLS-SEM). The current paper reviews PLS-SEM and its algorithm, and provides an overview of when it can be most appropriately applied, indicating its potential and limitations for future research. The authors conclude that PLS-SEM path modeling, if appropriately applied, is indeed a "silver bullet" for estimating causal models in many theoretical models and empirical data situations.

PLS-SEM: Indeed a Silver Bullet

Generalized Additive Models.

Donoho and Johnstone (1994) proposed a method for reconstructing an unknown function f on [0,1] from noisy data d/sub i/=f(t/sub i/)+/spl sigma/z/sub i/, i=0, ..., n-1,t/sub i/=i/n, where the z/sub i/ are independent and identically distributed standard Gaussian random variables. The reconstruction f/spl circ/*/sub n/ is defined in the wavelet domain by translating all the empirical wavelet coefficients of d toward 0 by an amount /spl sigma//spl middot//spl radic/(2log (n)/n). The authors prove two results about this type of estimator. [Smooth]: with high probability f/spl circ/*/sub n/ is at least as smooth as f, in any of a wide variety of smoothness measures. [Adapt]: the estimator comes nearly as close in mean square to f as any measurable estimator can come, uniformly over balls in each of two broad scales of smoothness classes. These two properties are unprecedented in several ways. The present proof of these results develops new facts about abstract statistical inference and its connection with an optimal recovery model. >

De-noising by soft-thresholding

We discuss the use of multivariate design to ensure representativity and balance of the training set data for PLS multivariate modeling. Three application areas are used to illustrate the discussio ...

The utility of multivariate design in PLS modeling

Image analysis and chemical information in images

Chemometrics was started around 30 years ago to cope with and utilize the rapidly increasing volumes of data produced in chemical laboratories. The methods of early chemometrics were mainly focused ...

New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production): with examples from pharmaceutical research and process modeling

A novel multiblock PLS algorithm called S-PLS (serial PLS) is presented. S-PLS models the separate predictor blocks serially, making it a supplement to hierarchical PLS. In the S-PLS algorithm the predictor blocks are connected only via the response Y. The block models are calculated using the Y residuals from the previous block model. This allows for an independent interpretation of the separate block models. In each block model the classical PLS algorithm is used. The principles of S-PLS are demonstrated on two chemical applications. In the first example, which is non-linear, S-PLS makes it possible to separate the linear and non-linear parts in the model. The second example illustrates how a model with two predictor blocks can be analysed with S-PLS. Copyright © 1999 John Wiley & Sons, Ltd.

Svante Wold

Papers

The utility of multivariate design in PLS modeling

Image analysis and chemical information in images

New and old trends in chemometrics. How to deal with the increasing data volumes in R&D&P (research, development and production): with examples from pharmaceutical research and process modeling

A serial extension of multiblock PLS

The chemometric analysis of point and dynamic data in pharmaceutical and biotech production (PAT) — some objectives and approaches