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

In QSAR analysis in environmental sciences, adverse effects of chemicals released to the environment are modelled and predicted as a function of the chemical properties of the pollutants. Usually t ...

On the selection of the training set in environmental QSAR analysis when compounds are clustered

The 20 naturally occurring amino acids are characterized by 20 variables: pKNH2, pKCOOH, pI, molecular weight, substituent van der Waals volume, seven 1H and 13C nuclear magnetic resonance shift variables, and eight hydrophobicity-hydrophilicity scales. The 20-dimensional data set is reduced to a few new dimensions by principal components analysis. The three first principal components reveal relationships between the properties of the amino acids and the genetic code. Thus the amino acids coded for by adenosine (A), uracil (U), or cytosine (C) in their second codon position (corresponding to U, A, or G in the second anticodon position) are grouped in these components. No grouping was detected for the amino acids coded for by guanine (G) in the second codon position (corresponding to C in the second anticodon position). The results show that a relationship exists between the physical-chemical properties of the amino acids and which of the A (U), U (A), or C (G) nucleotide is used in the second codon (anticodon) position. The amino acids coded for by G (C) in the second codon (anticodon) position do not participate in this relationship.

A multivariate study of the relationship between the genetic code and the physical-chemical properties of amino acids.

A technique is proposed for the quantitative determination of constituents in complex mixtures characterized by gas chromatography data. The technique is partial least squares in latent variables (PLS) and the data to which it is applied are gas chromatograms of the Aroclors 1242, 1248, 1252, and 1260. In addition the problem is formulated as one of classification and calibration and a sample of used transformer oil from a waste dump is classified as to its origin. 21 references, 2 figures, 3 tables.

Pattern recognition for classification and determination of polychlorinated biphenyls in environmental samples

Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

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Svante Wold

Papers

On the selection of the training set in environmental QSAR analysis when compounds are clustered

A multivariate study of the relationship between the genetic code and the physical-chemical properties of amino acids.

Pattern recognition for classification and determination of polychlorinated biphenyls in environmental samples

Orthogonal signal correction, wavelet analysis, and multivariate calibration of complicated process fluorescence data

Use of sacrificial anodes in synthetic electrochemistry. Processes involving carbon dioxide