Neural network self-organizing map

Includes an introduction to artificial intelligence, artificial neural networks, self-organizing maps, and growing cell structures... 

Kohonen artificial neural networks = Self-Organizing Maps... 

This format was developed in our group and is used fruitfully in SONNIA, software for producing Kohonen Self Organizing Maps (KSOM) and Coimter-Propaga-tion (CPG) neural networks for chemical application [6]. This file format is ASCII-based, contains the entire information about patterns and usually comes with the extension "dat . 

Now, one may ask, what if we are going to use Feed-Forward Neural Networks with the Back-Propagation learning rule Then, obviously, SVD can be used as a data transformation technique. PCA and SVD are often used as synonyms. Below we shall use PCA in the classical context and SVD in the case when it is applied to the data matrix before training any neural network, i.e., Kohonen s Self-Organizing Maps, or Counter-Propagation Neural Networks. 

Kohonen networks, also known as self-organizing maps (SOMs), belong to the large group of methods called artificial neural networks. Artificial neural networks (ANNs) are techniques which process information in a way that is motivated by the functionality of biological nervous systems. For a more detailed description see Section 9.5. 

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. 

Another approach for solving the problem of representing data points in an -dimensional measurement space involves using an iterative technique known as the Kohonen neural network [41, 42] or self-organizing map (SOM). A Kohonen neural network consists of a layer of neurons arranged in a two-dimensional grid or... 

Not all neural networks are the same their connections, elemental functions, training methods and applications may differ in significant ways. The types of elements in a network and the connections between them are referred to as the network architecture. Commonly used elements in artificial neural networks will be presented in Chapter 2. The multilayer perception, one of the most commonly used architectures, is described in Chapter 3. Other architectures, such as radial basis function networks and self organizing maps (SOM) or Kohonen architectures, will be described in Chapter 4. 

Self-organizing maps are, like most neural network applications, limited by the quality of the data that is used to train them. If the training data is not representative of the whole set of data to which a network is expected to apply, then the clusters found in training may not be representative. This is especially critical with small sets of training data. 

Some historically important artificial neural networks are Hopfield Networks, Per-ceptron Networks and Adaline Networks, while the most well-known are Backpropa-gation Artificial Neural Networks (BP-ANN), Kohonen Networks (K-ANN, or Self-Organizing Maps, SOM), Radial Basis Function Networks (RBFN), Probabilistic Neural Networks (PNN), Generalized Regression Neural Networks (GRNN), Learning Vector Quantization Networks (LVQ), and Adaptive Bidirectional Associative Memory (ABAM). 

Self-organizing maps (also called SOMs, Kohonen feature maps, or kmaps) are special kinds of artificial neural networks (ANNs) that are able to represent sets of descriptors in a low-dimensional map [114—116], and are increasingly applied for mapping of various molecular data in the fields of analytical chemistry and drug design [89, 117, 118]. 

J. Vesanto and E. Alhoniemi, Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11 586-600 (2000). 

A type of neural network that has been proved to be successful in a series of applications is based on self-organizing maps (SOMs) or Kohonen neural networks [61]. Whereas most of the networks are designed for supervised learning tasks (i.e., the relationship between input and output must be known in form of a mathematical model), Kohonen neural networks are designed primarily for unsupervised learning where no prior knowledge about this relationship is necessary [62,63]. 

Kohonen Neural Networks or self-organizing maps (SOMs) are a type of ANN designed for unsupervised learning where no prior knowledge about this relationship is necessary. 

Self-Organizing Maps (SOM) (= Kohonen maps, Kohonen artificial neural networks) Kohonen maps are self-organizing systems able to face the unsupervised rather than the supervised problems [Kohonen, 1989, 1990],... 

Cluster analysis methods, —> Principal Component Analysis and related techniques, and different —> artificial neural networks (such as Self-Organizing Maps) are usually used to search for clusters of similar compounds, a cluster being comprised of distinct objects that are more similar to each other than to any other object outside the group. 

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