Pattern recognition methods

Not every PRM is suitable for constructing spectral identification libraries. These are usually compiled by using supervised modeling methods, and unknown samples are identified with those classes they resemble most. 

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We present in this paper an invariant pattern recognition method, applied to radiographic images of welded joints for the extraction of feature vectors of the weld defects and their classification so that they will be recognized automatically by the inspection system. 

An invariant pattern recognition method, based on the Hartley transform, and applied to radiographic images, containing different types of weld defects, is presented. Practical results show that this method is capable to describe weld flaws into a small feature vectors, allowing their recognition automatically by the inspection system we are realizing. 

The successful appHcation of pattern recognition methods depends on a number of assumptions (14). Obviously, there must be multiple samples from a system with multiple measurements consistendy made on each sample. For many techniques the system should be overdeterrnined the ratio of number of samples to number of measurements should be at least three. These techniques assume that the nearness of points in hyperspace faithfully redects the similarity of the properties of the samples. The data should be arranged in a data matrix with one row per sample, and the entries of each row should be the measurements made on the sample, as shown in Figure 1. The information needed to answer the questions must be implicitly contained in that data matrix, and the data representation must be conformable with the pattern recognition algorithms used. 

Many current multidimensional methods are based on instruments that combine measurements of several luminescence variables and present a multiparameter data set. The challenge of analyzing such complex data has stimulated the application of special mathematical methods (80-85) that are made practical only with the aid of computers. It is to be expected that future analytical strategies will rely heavily on computerized pattern recognition methods (79, 86) applied to libraries of standardized multidimensional spectra, a development that will require that published luminescence spectra be routinely corrected for instrumental artifacts. Warner et al, (84) have discussed the multiparameter nature of luminescence measurements in detail and list fourteen different parameters that can be combined in various combinations for simultaneous measurement, thereby maximizing luminescence selectivity with multidimensional measurements. Table II is adapted from their paper with the inclusion of a few additional parameters. 

Oldfield TJ. Pattern-recognition methods to identify secondary structure within X-ray crystallographic electron-density maps. Acta Cryst. 2002 058 487-93. 

There are many types of pattern recognition which essentially differ in the way they define classification rules. In this section, we will describe some of the approaches, which we will then develop further in the following sections. We will not try to develop a classification of pattern recognition methods but merely indicate some characteristics of the methods, that are found most often in the chemometric literature and some differences between those methods. 

Yeh and Spiegelman [24], Very good results were also obtained by using simple neural networks of the type described in Section 33.2.9 to derive a decision rule at each branching of the tree [25]. Classification trees have been used relatively rarely in chemometrics, but it seems that in general [26] their performance is comparable to that of the best pattern recognition methods. 

This is the simplest possible type of neuron, used here for didactic purposes and not because it is the configuration to be recommended. Let us suppose that for this isolated neuron w, = 1, Wj = 2 and 7=1. The line in Fig. 33.20 then gives the values of x, and Xj for which E = 7. All combinations of x, and Xj on and above the line will yield E > 7 and therefore lead to an output y, = 1 (i.e. the object is class K), all combinations below it toy, = 0. The procedure described here is equivalent to a method called the linear learning machine, which was one of the first supervised pattern recognition methods to be applied in chemometrics. It is further explained, including the training phase, in Chapter 44. 

E. Saaksjarvi, M. Khaligi and P. Minkkinen, Waste water pollution modeling in the southern area of Lake Saimaa, Finland, by the simca pattern recognition method. Chemom. Intell. Lab. Systems, 7(1989) 171-180. 

X. H. Song and P.K. Hopke, Kohonen neural network as a pattern-recognition method, based on weight interpretation. Anal. Chim. Acta, 334 (1996) 57-66. 

PATTERN RECOGNITION METHODS FOR OBJECTIVELY CLASSIFYING BACTERIA... 

Two examples of unsupervised classical pattern recognition methods are hierarchical cluster analysis (HCA) and principal components analysis (PCA). Unsupervised methods attempt to discover natural clusters within data sets. Both HCA and PCA cluster data. 

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

Danzer K, Singer R (1985) Application of pattern recognition methods for the investigation of chemical homogeneity of solids. Mikrochim Acta [Wien] 1 219... 

Borszeki J, Kepes J, Koltay L, Sarudi I (1986a) Classification of paprika quality using pattern recognition methods based on elemental composition. Acta Alimentaria 15 93... 

Juricskay I, Veress GE (1985) PRIMA a new pattern recognition method. Anal Chim Acta 171 61... 

Fu, J., et al., A pattern recognition method for electronic noses based on an olfactory neural network, Sens. Actuat. B Chem., 125, 489,2007. 

Network as a Pattern Recognition Method Based on the Weight Interpretation. 

Furthermore, as a fuel evaporates or biodegrades, its pattern can change so radically that identification becomes difficult. Consequently, a gas chromatographic fingerprint is not a conclusive diagnostic tool. The methods used for total petroleum hydrocarbon analysis must stress calibration and quality control, whereas pattern recognition methods stress detail and comparability. 

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