Journal ArticleDOI
Kohonen and counterpropagation artificial neural networks in analytical chemistry
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TLDR
The principles of the Kohonen and counterpropagation artificial neural network (K-ANN and CP-ANN) learning strategy is described and the use of both methods is explained with several examples from analytical chemistry.About:
This article is published in Chemometrics and Intelligent Laboratory Systems.The article was published on 1997-08-01. It has received 250 citations till now. The article focuses on the topics: Counterpropagation network & Hybrid Kohonen self-organizing map.read more
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Journal ArticleDOI
Principles of QSAR models validation: internal and external
TL;DR: Evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes.
Journal ArticleDOI
Classification tools in chemistry. Part 1: linear models. PLS-DA
Davide Ballabio,Viviana Consonni +1 more
TL;DR: The common steps to calibrate and validate classification models based on partial least squares discriminant analysis are discussed in the present tutorial, and issues to be evaluated during model training and validation are introduced and explained using a chemical dataset.
Journal ArticleDOI
Variable selection in near-infrared spectroscopy: Benchmarking of feature selection methods on biodiesel data
TL;DR: This paper compares the performance of 16 different feature selection methods for the prediction of properties of biodiesel fuel, including density, viscosity, methanol content, and water concentration.
Journal ArticleDOI
Statistical external validation and consensus modeling: a QSPR case study for Koc prediction.
TL;DR: The soil sorption partition coefficient (log K(oc)) of a heterogeneous set of 643 organic non-ionic compounds, with a range of more than 6 log units, is predicted by a statistically validated QSAR modeling approach.
Journal ArticleDOI
Does rational selection of training and test sets improve the outcome of QSAR modeling
Todd M. Martin,Paul Harten,Douglas M. Young,Eugene N. Muratov,Eugene N. Muratov,Alexander Golbraikh,Hao Zhu,Alexander Tropsha +7 more
TL;DR: The results of this study indicate that models based on rational division methods generate better statistical results for the test sets than modelsbased on random division, but the predictive power of both types of models are comparable.
References
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An introduction to computing with neural nets
TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Book
Scanning Electron Microscopy and X-Ray Microanalysis
Joseph I. Goldstein,Dale E. Newbury,J. R. Michael,Nicholas W. M. Ritchie,John Henry J. Scott,David C. Joy +5 more
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Chemometrics: A Textbook
TL;DR: This chapter discusses Chemometrics and the Analytical Process, a large-scale comparison of two procedures for optimization of Analytical Chemical Methods, and its applications in Operations Research and Decision Making.
Journal ArticleDOI
An introduction to neural computing
TL;DR: A brief survey of the motivations, fundamentals, and applications of artificial neural networks, as well as some detailed analytical expressions for their theory.
Self-organization and associative memory
TL;DR: In this paper, the problem of infinite-state memory is addressed in the context of biological memory using an adaptive filtering approach based on the classical laws of association, which is used for the purpose and nature of the biological memory.