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

Synthese d'alcoxy-4 chloro-5 tetrahydro-1,2,3,4 [oxo-3 thiadiazole-1,2,4ine-4yl-5]-1 pyrimidones-2 a partir de chloro-5 thiadiazole-1,2,4ine-4ones-3 et de chloro-5 pyrimidinolate dans des solvants alcanols-1

Covalent adduct formation in 1,2,4-thiadiazolo activated pyrimidinones

Partition coefficient data (logP) for 121 benzene derivatives from the 1-octanol/water system were subjected to principal components analysis (PCA). The statistical analysis shows an interaction between substituents on a benzene nucleus that affects logP values in a measurable way. The interaction can be modelled by Hammet's ϕ, and is probably due to differences in solvent-solute hydrogen bonding in the two phases, 1-octanol and water. The significance of these results for the prediction of logP values is discussed.

Nonadditivity of 1‐Octanol/Water Partition Coefficients of Disubstituted Benzenes: An Explanation and Its Consideration in Log P Estimation

The last decade has witnessed much progress in how to characterize and describe chemical structure, how to synthesize large sets of compounds, how to make simple and fast in-vitro assays, and how to determine the structure (sequence) of our genetic material. The possible consequences of this progress for drug design are great and exciting, but also bewilderingly complicated.

Multivariate Design and Modelling in QSAR, Combinatorial Chemistry, and Bioinformatics

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Chlorophylls. VII. A 1H NMR Spectroscopic Study of the Phase-Test Intermediate of Chlorophyll a.

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Svante Wold

Papers

Covalent adduct formation in 1,2,4-thiadiazolo activated pyrimidinones

Nonadditivity of 1‐Octanol/Water Partition Coefficients of Disubstituted Benzenes: An Explanation and Its Consideration in Log P Estimation

Multivariate Design and Modelling in QSAR, Combinatorial Chemistry, and Bioinformatics

Chlorophylls. VII. A 1H NMR Spectroscopic Study of the Phase-Test Intermediate of Chlorophyll a.

The Crystal Structure and Ionic Conductivity Properties of K2ZnGe2O6.