Linear learning machine analysis

In the class discrimination methods or hyperplane techniques, of which linear discriminant analysis and the linear learning machine are examples, the equation of a plane or hyperplane is calculated that separates one class from another. These methods work well if prior knowledge allows the analyst to assume that the test objects must... 

Among the nonparametric techniques of pattern recognition, the linear learning machines have been only seldom used in food data analysis and it seems that this method is becoming obsolete. 

Supervised learning methods - multivariate analysis of variance and discriminant analysis (MVDA) - k nearest neighbors (kNN) - linear learning machine (LLM) - BAYES classification - soft independent modeling of class analogy (SIMCA) - UNEQ classification Quantitative demarcation of a priori classes, relationships between class properties and variables... 

Canonical Correlation Analysis Principal Component Regressionb Classification and Regression Trees (CART) Linear Learning Machine Neural Networks Adaptive Least Squares Genetic Programming Logistic Regression... 

If the membership of objects to particular clusters is known in advance, the methods of supervised pattern recognition can be used. In this section, the following methods are explained linear learning machine (LLM), discriminant analysis, A -NN, the soft independent modeling of class analogies (SIMCA) method, and Support Vector Machines (SVMs). 

Discriminant analysis, also known as the linear learning machine, is intended for use with classified dependent data. The data may be measured on a nominal scale (yes/no, active/inactive, toxic/non-toxic) or an ordinal scale (1,2,3,4 active, medimn, inactive) or may be derived from continuous data by some rule (such as low if <10, high if > 10). The objective of... 

Linear discriminant analysis is equivalent to the linear learning machine. There are also procedures for non-linear discriminant analysis (as there are for non-linear regression) but these will not be considered here. 

S.J. Dixon and R.G. Brereton, Comparison of performance of five common classifiers represented as boundary methods Euclidean distance to centroids, linear discriminant analysis, quadratic discriminant analysis, learning vector quantization and support vector machines, as dependent on data structure, Chemom. Intell. Lab. Syst, 95, 1-17 (2009). 

A more common use of informatics for data analysis is the development of (quantitative) structure-property relationships (QSPR) for the prediction of materials properties and thus ultimately the design of polymers. Quantitative structure-property relationships are multivariate statistical correlations between the property of a polymer and a number of variables, which are either physical properties themselves or descriptors, which hold information about a polymer in a more abstract way. The simplest QSPR models are usually linear regression-type models but complex neural networks and numerous other machine-learning techniques have also been used. 

In organic chemistry, decomposition of molecules into substituents and molecular frameworks is a natural way to characterize molecular structures. In QSAR, both the Hansch-Fujita " and the Free-Wilson classical approaches are based on this decomposition, but only the second one explicitly accounts for the presence or the absence of substituent(s) attached to molecular framework at a certain position. While the multiple linear regression technique was associated with the Free-Wilson method, recent modifications of this approach involve more sophisticated statistical and machine-learning approaches, such as the principal component analysis and neural networks. ... 

These considerations provide an impetus for the development of fast, nonlinear, variable selection QSAR methods that can avoid the aforementioned problems of linear QSAR. Several nonlinear QSAR methods have been proposed in recent years. Most of these methods are based on either artificial neural network (ANN) (50, 61, 137-142) or machine learning techniques (65,143-145). Given that optimization of many parameters is involved in these techniques, the speed of the analysis is relatively slow. More recently. Hirst reported a simple and fast nonlinear QSAR method (146), in which the activity surface was generated from the activities of training set compounds based on some predefined mathematical function. 

Linear discriminant analysis (LDA) is used in statistics and machine learning methods to find the best linear combination of descriptors that distinguish two or more classes of objects or events, and, in the present case, to distinguish between substrates and nonsubstrates of P-gp. A linear classifier achieves this by making a classification decision based on the value of the linear combination of descriptors. 

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