Kohonen self-organized maps SOMs

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. 

MOLMAP (Molecular Map of Atom-level Properties) descriptors are uniform-length vectorial descriptors derived by mapping physico-chemical properties of all the bonds in a molecule into a 2D Kohonen —> self organizing map (SOM) [Zhang and Aires-de-Sousa, 2005 Gupta, Metthew ef al., 2006]. These descriptors encode local features of a chemical structure, being calculated on the basis of properties of single elements in a molecule, such as bonds. 

Neural nets can also be based on unsupervised learning strategies. To date these nets have been employed primarily to support data visualization, but their flexibility is such that they are becoming more common in a wide variety of applications. A simple version of an unsupervised neural net is the Kohonen self-organizing map (SOM) (Kohonen, 1982, 1984 Lang, this volume). These nets also use a set number of nodes, but operate according to different principles. 

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. 

The Kohonen network or self-organizing map (SOM) was developed by Teuvo Kohonen [11]. It can be used to classify a set of input vectors according to their similarity. The result of such a network is usually a two-dimensional map. Thus, the Kohonen network is a method for projecting objects from a multidimensional space into a two-dimensional space. This projection keeps the topology of the multidimensional space, i.e., points which are close to one another in the multidimensional space are neighbors in the two-dimensional space as well. An advantage of this method is that the results of such a mapping can easily be visualized. 

For many problems, rather different solutions of similar fitness exist. To obtain a quick overview of the structure of the underlying high-dimensional fitness landscape, we used a self-organizing map (SOM) that was developed originally by Kohonen [14] in this study. Figure 10.1 shows a nonlinear mapping of a seven-... 

Unsupervised multivariate statistical methods [CA, principal components analysis, Kohonen s self-organizing maps (SOMs), nonlinear mapping, etc.], which perform spontaneous data analysis without the need for special training (learning), levels of knowledge, or preliminary conditions. 

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. 

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). 

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],... 

Kohonen networks or self-organizing maps (SOMs) are obtained by a complex, highly nonlinear mathematical approach for dimensionality reduction [102]. After training those networks produce a 2D-map with regions containing similar molecules. As a result of the complex mathematical formalism, models produced by nonlinear projection approaches are often more accurate maintaining the local environment of a molecule, while straightforward interpretation is less obvious. 

Self-Organizing Maps (SOMs) or Kohonen maps are types of Artificial Neural Networks (ANNs) that are trained using supervised/unsupervised learning to produce a low-dimensional discretized representation (typically 2-dimensional) of an arbitrary dimension of input space of the training samples (Zhong et al. 2005). 

The self-organizing map (SOM) (Kohonen, 1982) is a clustering network that works differently from the MLP. The SOM is made up of a grid of neurons. Each neuron contains a reference vector, which is a set of numbers the same size as the input. 

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]. 

Kohonen networks (SOM—self organizing maps) have the ability to self-leam and can acquire knowledge without a teacher. 

A specialized method for similarity-based visualization of high-dimensional data is formed by self-organizing feature maps (SOM). The data items are arranged on a two-dimensional plane with the aid of neural networks, especially Kohonen nets. Similarity between data items is represented by spacial closeness, while large distances indicate major dissimilarities [968]. At the authors department, a system called MIDAS had already been developed which combines strategies for the creation of feature maps with the supervised generation of fuzzy-terms from the maps [967]. 

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