Artificial neural networks other regression

In most cases, the MFTA models are built using the Partial Least Squares Regression (PLSR) technique that is suitable for the stable modeling based on the excessive and/or correlated descriptors (under-defined data sets). However, the MFTA approach is not limited to the PLSR models and can successfully employ other statistical learning techniques such as the Artificial Neural Networks (ANN) supporting the detection of the nonlinear structure-activity relationships. ... 

Methods other than thermodynamic cycles are often used to calculate acid dissociation constants. Previous publications implement the theoretical relationship between pKa and structural property [6], bond valence methods and bond lengths [33], pKa correlations with highest occupied molecular orbital (HOMO) energies and frontier molecular orbitals [34], and artificial neural networks [35] to predict pKa values. In addition much work has been done using physical properties as quantitative structure-activity relationship (QSAR) descriptors, and regression equations with such descriptors to yield accurate pKa values for specific classes of molecules [36-47]. The correlation of pKas to various molecular properties, however, is often restricted to specific classes of compounds, and it is... 

In addition to the approaches described above, a number of algorithms using PSA and other analytes have been developed to increase the sensitivity of prostate cancer detection, They include logistic regression and artificial neural networks. 

Several stochastic models, based on mutli-parametric regression, artificial neural networks, Kalman filter and other statistical techniques, were implemented for short-term forecast of air pollution episodes, namely high ozone concentrations (Czech Republic, Hungary, Poland, Slovenia). 

Finally, Chapter 6 goes into two new regression paradigms artificial neural networks and support vector machines. Quite different from the other regression methods presented in the book, they are gaining acceptance because they can handle non-linear systems and/or noisy data. This step forward is introduced briefly and, once more, a review is presented with practical applications in the atomic spectroscopy field. Not surprisingly, most papers deal with complex measurements [e.g. non-linear calibration or... 

A similar situation exists in the area of data mining. Nonlinear regression models computed by artificial neural networks are likely to be supplemented by other modem nonhnear regression models, in particular ... 

Before the development of SVM techniques, there are two usually used techniques for the data processing of nonlinear data sets. One is nonlinear regression with polynomials, and the other is artificial neural network. It is well known that the former often needs too many terms and too many adjustable parameters in regression. This is so-called curse of dimensionalily . And the latter often suffers from overfitting, i.e., having low reliability of the prediction results. 

In the past, people was apt to think that the best way to increase the prediction ability of the mathematical models obtained from data processing is to find a function to fit the training data set as close as possible. In other words, best training could assure best prediction result. But this concept has been found to be not correct in the practice of the application work of artificial neural networks or nonlinear regression with polynomial equations. Therefore, it has become an imminent task to find a strict mathematical theory for solving the problem of overfitting [68]. 

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