Algorithm recognition

Algorithm, recognition Computer programs or instruction sets for the recognition of specific phenomena from a processing of data acquired for the system from some external source. 

Problems involving routine calculations are solved much faster and more reliably by computers than by humans. Nevertheless, there are tasks in which humans perform better, such as those in which the procedure is not strictly determined and problems which are not strictly algorithmic. One of these tasks is the recognition of patterns such as feces. For several decades people have been trying to develop methods which enable computers to achieve better results in these fields. One approach, artificial neural networks, which model the functionality of the brain, is explained in this section. 

Other methods consist of algorithms based on multivariate classification techniques or neural networks they are constructed for automatic recognition of structural properties from spectral data, or for simulation of spectra from structural properties [83]. Multivariate data analysis for spectrum interpretation is based on the characterization of spectra by a set of spectral features. A spectrum can be considered as a point in a multidimensional space with the coordinates defined by spectral features. Exploratory data analysis and cluster analysis are used to investigate the multidimensional space and to evaluate rules to distinguish structure classes. 

Jones G, P Willett and R C Glen 1995b. Molecular Recognition of Receptor Sites Using a Geneti Algorithm with a Description of Desolvation. Journal of Molecular Biology 245 43-53. 

In the field of chemical sensors, the revolution in software and inexpensive hardware means that not only nonlinear chemical responses can be tolerated, but incomplete selectivity to a variety of chemical species can also be handled. Arrays of imperfectly selective sensors can be used in conjunction with pattern recognition algorithms to sort out classes of chemical compounds and thek concentrations when the latter are mixed together. 

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. 

Brooijmans N, Knntz ID. Molecular recognition and docking algorithms. Anna Rev Biophys Biomol Struct 2003 32 335-73. 

The applicability of a clustering algorithm to pattern recognition is entirely dependent upon the clustering characteristics of the patterns in the representation space. This structural dependence emphasizes the importance of representation. An optimal representation uses pattern features that result in easily identified clustering of the different pattern classes in the representation space. At the other extreme, a poor choice of representation can result in patterns from all classes being uniformly distributed with no discernible class structure. 

Carpenter, G. A., Grossberg, S., and Rosen, D. B., ART2-A An adaptive resonance algorithm for rapid category learning and recognition, Neural Network 4, 493 (1990). 

Image analysis is an important aspect of many areas of science and engineering, and imaging will play an important role in characterizing self-assembled structures as well as in on-line process control. Development of effective noise identification and suppression, contrast enhancements, visualization, pattern recognition, and correlation algorithms should be co-opted where possible and adapted to the analysis of self-assembled structures. 

Jones, G., Willett, P., and Glen, R. C. (1995) Molecular recognition of receptor sites using a genetic algorithm with a description of desolvation. J. Mol. Biol. 245,43-53. 

Image Recognition and Classification Algorithms, Systems, and Applications, edited by Bahram Javidi... 

Elirman, L.M. and Lanterman, A.D. A robust algorithm for automatic target recognition using passive radar Proceedings of the Thirty-Sixth Southeastern Symposium on System Theory, 2004 pp 102-106, March 14-16, 2004. 

Pattern recognition applied to several disciplines and practical problems produced a huge number of algorithms and techniques that are, in... 

---