Neural unsupervised

The Kohonen network i.s a neural network which uses an unsupervised learning strategy. Sec Section 9.5,3 for a more detailed description. 

The main characteristics of the method, developed in our group for reaction classification arc 1) the representation of a reaction by physicochemical values calculated for the bonds being broken and made during the reaction, and 2 use of the unsupervised learning method of a self-organi2ing neural network for the perception of similarity of chemical reactions [3, 4],... 

As described in the Introduction to this volume (Chapter 28), neural networks can be used to carry out certain tasks of supervised or unsupervised learning. In particular, Kohonen mapping is related to clustering. It will be explained in more detail in Chapter 44. 

R. Goodacre, J. Pygall and D.B. Kell, Plant seed classification using pyrolysis mass spectrometry with unsupervised learning the application of auto-associative and Kohonen artificial neural networks. Chemom. Intell. Lab. Syst., 33 (1996) 69-83. 

Oja, E., Unsupervised neural learning, in Neural Networks for Chemical Engineers (A. B. Bulsari, (ed.). Elsevier, Amsterdam, 1995, p. 21. 

Another type of ANNs widely employed is represented by the Kohonen self organizing maps (SOMs), used for unsupervised exploratory analysis, and by the counterpropagation (CP) neural networks, used for nonlinear regression and classification (Marini, 2009). Also, these tools require a considerable number of objects to build reliable models and a severe validation. 

Fig. 10.11 Representation of hybrid approach in which Kohonen maps, neural nets, multiple component analysis, and pattern recognition are combined to create a complex data evaluation cascade. Within this cascade supervised (quantitative) and unsupervised (qualitative) techniques are combined (Hierlemann et al., 1996)...

Fritzke, B. (1994) Growing cell structures-a self-organizing network for unsupervised and supervised learning. Neural Networks 7 1441-1460... 

Sanger, T.D. (1989) Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural Networks, 2, 359 173. 

There are literally dozens of kinds of neural network architectures in use. A simple taxonomy divides them into two types based on learning algorithms (supervised, unsupervised) and into subtypes based upon whether they are feed-forward or feedback type networks. In this chapter, two other commonly used architectures, radial basis functions and Kohonen self-organizing architectures, will be discussed. Additionally, variants of multilayer perceptrons that have enhanced statistical properties will be presented. 

There are two different approaches to the protein sequence classification problem. One can use an unsupervised neural network to group proteins if there is no knowledge of the number and composition of final clusters (e.g., Ferran Ferrara, 1992). Or one can use supervised networks to classify sequences into known (existing) protein families (e.g., Wu et al., 1992). 

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