Pattern recognition analogies

Classical supervised pattern recognition methods include /( -nearest neighbor (KNN) and soft independent modeling of class analogies (SIMCA). Both... 

Soft Independent Method of Class Analogy (SIMCA), a pattern recognition technique based on principal components (25) was selected to evaluate and apply to the problems of establishing similarities among sample residue profiles. The development of a laboratory data management system to assist in the calculation and organization of results greatly enhanced the feasibility of this approach (26). 

Application of Soft Independent Modeling of Class Analogy Pattern Recognition to Air Pollutant Analytical Data... 

Simple Modeling by Chemical Analogy Pattern Recognition... 

In the nineteen-seventies, new methods for pattern recognition have been developed by means of quantitative analogy models. The analogy analysis implies looking for regularities in the observations made. The individual items that are analyzed are called objects. The objects are alike that will be brought into the same class. In our case the number of classes would be two, the two barley cultivars (Etu,Tellus). The samples obtained from soil are used as test objects. 

Current methods for supervised pattern recognition are numerous. Typical linear methods are linear discriminant analysis (LDA) based on distance calculation, soft independent modeling of class analogy (SIMCA), which emphasizes similarities within a class, and PLS discriminant analysis (PLS-DA), which performs regression between spectra and class memberships. More advanced methods are based on nonlinear techniques, such as neural networks. Parametric versus nonparametric computations is a further distinction. In parametric techniques such as LDA, statistical parameters of normal sample distribution are used in the decision rules. Such restrictions do not influence nonparametric methods such as SIMCA, which perform more efficiently on NIR data collections. 

In supervised pattern recognition, a major aim is to define the distance of an object from the centre of a class. There are two principle uses of statistical distances. The first is to obtain a measurement analogous to a score, often called the linear discriminant function, first proposed by the statistician R A Fisher. This differs from the distance above in that it is a single number if there are only two classes. It is analogous to the distance along line 2 in Figure 4.26, but defined by... 

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). 

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