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Self-organising map

Cartwright, H.M., Investigation of structure-biodegradability relationships in polychlorinated biphenyls using self-organising maps, Neural Comput. Apps., 11, 30, 2002. [Pg.8]

T. Kohonen Self Organising Map, 1995 Springer Verlag, Berlin Germany... [Pg.168]

Drift Counteraction with Multiple Self-Organising Maps for an Electronic Nose. [Pg.389]

Another alternative approach was proposed by Kalelkar et al These authors acknowledged the reality that no current automated approach was 100% reliable and that some level of human intervention was going to be required. The purpose of the automation was therefore to confirm the majority of wells within an MBS plate, but more particularly, to identify the minority that required the scrutiny of an experienced spectroscopist. Their approach was not to attempt to analyse the individual spectra but using a Self Organising Map (SOM), to ... [Pg.236]

It is, at this point, important to understand the difference between unsupervised methods and supervised methods. With the former, there is no indication given to the model creation program (e.g. PCA, self-organising maps) of where any of... [Pg.106]

Table 1 Typical uses of artificial intelligence in chemistry. Genetic algorithm (GA), neural network (NN), self-organising map (SOM), knowledge-based system (KBS)... Table 1 Typical uses of artificial intelligence in chemistry. Genetic algorithm (GA), neural network (NN), self-organising map (SOM), knowledge-based system (KBS)...
Kaiser, D.,Terfloth, L., Kopp, S., de Laet, R., Chiba, P., Ecker, G.F., and Gasteiger, J. (2007) Self-organising maps for identification of new inhibitors of P-glycoprotein. Journal of Medicinal Chemistry, 50, 1698-1702. [Pg.361]

Meissen, W., tlstiin, B. and Buydens, L. (2007) SOMPLS a supervised self-organising map-partial least squares algorithm for multivariate regression problems. Chemom. Intell Lab. Syst., 86, 102-120. [Pg.1120]

Bernard P, Golbraikh A, Kireev D, Chretien JR, Rozhkova N. Comparison of chemical databases analysis of molecular diversity with self organising maps (SOM). Analusis 1998 26 333-341. [Pg.694]

In 2008 Borah et al. [38] proposed that Neural Network based E-Nose, comprising of an array of four tin-oxide gas sensors, can assist tea quality monitoring during quality grading, principal component analysis (PCA) was used to visualise the different aroma profiles. In addition, K-means and Kohonen s self organising map (SOM) cluster analysis was done, multi layer Perceptron (MLP) network, radial basis function (RBF) network, and constructive probabilistic neural network (CPNN) were used for aroma classification [38]. [Pg.106]

A method for visualising the extent of similarity amongst a set of crystal structures has been developed from the WCC approach, whereby stmctures are represented in a self-organising map [49]. The underlying principle is that similar structures are binned into subsets so that a continuous space of 475 000 unique CSD entries might be reduced... [Pg.28]

As an example of the approach [49], a self-organising map comprising 1600 bins was generated from a sample database of 11165 CSD entries selected from the April 2004 database, and three distinct sets of structures were compared with the trained map ... [Pg.29]

Figure 1.15 Mapping of (a) 2303 steroids structures and (b) the 1262 peptide structures onto a self-organising map comprising 1600 bins trained with 11 165 structures from the April 2004 CSD. The scale on the left-hand side indicates the number of structures mapped to each bin. No compounds are mapped to white bins. In both cases, the structures concentrate in specific (but diffuse) regions of the map. Reproduced from [49] by permission of the International Union of Crystallography, (see colour plate section)... Figure 1.15 Mapping of (a) 2303 steroids structures and (b) the 1262 peptide structures onto a self-organising map comprising 1600 bins trained with 11 165 structures from the April 2004 CSD. The scale on the left-hand side indicates the number of structures mapped to each bin. No compounds are mapped to white bins. In both cases, the structures concentrate in specific (but diffuse) regions of the map. Reproduced from [49] by permission of the International Union of Crystallography, (see colour plate section)...
In a manner reminiscent of the self-organising maps, the methodology has been applied to produce a subset of the database that represents the best representative of each unique crystal stracture [53]. Thus, a compound with 10 CSD entries, comprising one polymorph determined seven times and a second polymorph determined three times will be reduced to two entries, which are considered to represent the two unique structure types. The details of the applied quality tests are extensive [53], but the result is a list of 231918 structures (derived from 353 666 structures in the November 2005 release) that are considered to be the best representative examples of all unique high-quality stmctures in the CSD [54], In this way, the complete contents of the CSD are reduced to a set of representative structures that contain an equivalent amount of structural information, but without any redundancy. This dataset forms an especially convenient basis for structural searches, since it is free of any duplication. [Pg.32]

Conceptually, the process is equivalent to the generation of self-organised maps. In the case of the best representative set, the conditions for two structures to fall into the same bin include the strict criterion of identical chemical struaure. Thus, only redeterminations of the same crystal structure fall into the same bin, and the number of bins in the final map will be equal to the number of unique structures in the CSD. [Pg.32]

R. Wehrens, W. Meissen, L. Buydens and R. de Gelder, Representing structural databases in a self-organising map, Acta Cryst., B61, 548-557 (2005). [Pg.40]

Fig. 6 A self-organising map from toxicity profiles, showing three clusters of metal oxide nanopartieles (I non-toxic II, III toxic response). Reproduced with permission from ref. 31. Copyright 2013 Royal Society of Chemistry... Fig. 6 A self-organising map from toxicity profiles, showing three clusters of metal oxide nanopartieles (I non-toxic II, III toxic response). Reproduced with permission from ref. 31. Copyright 2013 Royal Society of Chemistry...
Comparison of Molecular Databases Analysis of Molecular Diversity with Self-Organising Maps (SOM). [Pg.136]

Finally, one class of unsupervised methods is represented by self-organising maps (SOM), or Kohonen maps, named after the Finnish professor Teuvo Kohonen. A SOM is a type of artificial neural network that needs to be trained but does not require labelling of the input vectors. Examples of classification analysis by SOMs in biomedical IR and Raman spectroscopy are given in references. ... [Pg.213]

Kurd, Z., Kelly, T. Using Euzzy Self-Organising Maps for Safety Critical Apphcations. Reliability Engineering System Safety 92(11), 1563—1583 (2007)... [Pg.340]

Kohonen, T. (2001) Self-Organising Maps. Springer Series in Information Sciences, 3 edition, volume 30, Springer, Berlin, 501 pp. [Pg.113]


See other pages where Self-organising map is mentioned: [Pg.301]    [Pg.505]    [Pg.212]    [Pg.104]    [Pg.28]    [Pg.28]    [Pg.379]    [Pg.84]    [Pg.284]   
See also in sourсe #XX -- [ Pg.236 ]




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