The Self-Organizing Map (SOM) algorithm has attracted an ever increasing amount of interest among researches and practitioners in a wide variety of elds The SOM and a variant of it, the LVQ, have been analyzed extensively, a number of variants of them have been developed and, perhaps most notably, they have been applied extensively within elds ranging from engineering sciences to medicine, biology, and economics We have collected a comprehensive list of 3343 scienti c papers that use the algorithms, have bene ted from them, or contain analyses of them The list is intended to serve as a source for literature surveys We have provided both a thematic and a keyword index to help nding articles of interest

Bibliography of Self-Organizing Map (SOM) Papers: 1981-1997

Our ultimate objective in data mining is to identify any hidden patterns or relationships between our data elements, and in one sense, machine learning provides us with a set of techniques to do just that: techniques that allow us to learn the patterns without any outside influence (unsupervised learning). However, just as is the case with anything, that power comes at a price, but the results can be very interesting and very significant. In other cases, we have some sense on what the results should be and so can guide the learning techniques through an initial “training” phase, directing our system and honing the results (supervised learning). These two broad classifications of machine-learning methods will ground us as we discuss a broad range of techniques and where they are currently being applied in life sciences research, expanding our toolkit and enabling us to take a very different path in our analysis efforts: using an artificial intelligence discipline and letting the data tell us what it contains. As datasets grow, these techniques become more important.

Machine-Learning Techniques

We present a modification of Kohonens SOFMin order to simulate cortical plasticity induced by coactivation patterns. This is accomplished by introducing a probabilistic mode of stimulus presentation and by substituting the winner-takes- all mechanism by selecting the winner from a set of best matching neurons.

Modification of Kohonen's SOFM to Simulate Cortical Plasticity Induced by Coactivation Input Patterns

Various applications of the mean field theory (MFT) technique for obtaining solutions close to optimal minima in feedback networks are reviewed. Using this method in the context of the Boltzmann machine gives rise to a fast deterministic learning algorithm with a performance comparable with that of the backpropagation algorithm (BP) in feature recognition applications. Since MFT learning is bidirectional its use can be extended from purely functional mappings to a content addressable memory. The storage capacity of such a network grows like O (10–20)nH with the number of hidden units. The MFT learning algorithm is local and thus it has an advantage over BP with respect to VLSI implementations. It is also demonstrated how MFT and BP are related in situations where the number of input units is much larger than the number of output units. In the context of-finding good solutions to difficult optimization problems the MFT technique again turns out to be extremely powerful. The quality of the solution...

Mean Field Theory Neural Networks for Feature Recognition, Content Addressable Memory and Optimization

The competitive learning technique is a well-known algorithm used in neural networks which classifies the input vectors, so that the vectors (samples) belonging to the same class have similar characteristics. Each class is represented by one unit. Dynamic competitive learning is an unsupervised learning technique consisting of two additional parts related to conventional competitive learning: a method of generation of new units within a cluster and a method of generating new clusters. As seen in a description of the multilayered neural networks, the number of clusters, their connections, and the generation of new units is determined dynamically during learning. The model is capable of high-level storage of complex data structures and their classification, including exception handling.© (1991) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Knowledge representation by dynamic competitive learning techniques

The performance of several neural-network-like models for pattern recognition tasks is analysed. A comparison based on recognition of random points in a multidimensional space is made between backpropagation, learning vector quantisation and three variations of Boltzmann machine models. The ordinary Boltzmann machine is found to perform well although at the expense of being time consuming. Replacement of the Monte Carlo method in the Boltzmann machine by a direct formula promises faster evaluation without significant loss of precision.

Variations on the Boltzmann machine

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Variations on the Boltzmann machine