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@Book{ Kriesel2007NeuralNetworks, author = { David Kriesel }, title = { A Brief Introduction to Neural Networks }, year = { 2007 }, url = { available at http://www.dkriesel.com } }Again, this reference is for the English version.
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Neural networks are a bio-inspired mechanism of data processing, that enables computers to learn technically similar to a brain and even generalize once solutions to enough problem instances are tought.
The manuscript „A Brief Introduction to Neural Networks“ is divided into several parts, that are again split to chapters. The contents of each chapter are summed up in the following.
How to teach a computer? You can either write a rigid program – or you can enable the computer to learn on its own. Living beings don't have any programmer writing a program for developing their skills, which only has to be executed. They learn by themselves – without the initial experience of external knowledge – and thus can solve problems better than any computer today. KaWhat qualities are needed to achieve such a behavior for devices like computers? Can such cognition be adapted from biology? History, development, decline and resurgence of a wide approach to solve problems.
How do biological systems solve problems? How is a system of neurons working? How can we understand its functionality? What are different quantities of neurons able to do? Where in the nervous system are information processed? A short biological overview of the complexity of simple elements of neural information processing followed by some thoughts about their simplification in order to technically adapt them.
Formal definitions and colloquial explanations of the components that realize the technical adaptations of biological neural networks. Initial descriptions of how to combine these components to a neural network.
Approaches and thoughts of how to teach machines. Should neural networks be corrected? Should they only be encouraged? Or should they even learn without any help? Thoughts about what we want to change during the learning procedure and how we will change it, about the measurement of errors and when we have learned enough.
A classic among the neural networks. If we talk about a neural network, then in the majority of cases we speak about a percepton or a variation of it. Perceptrons are multi-layer networks without recurrence and with fixed input and output layers. Description of a perceptron, its limits and extensions that should avoid the limitations. Derivation of learning procedures and discussion about their problems.
RBF networks approximate functions by stretching and compressing Gaussians and then summing them spatially shifted. Description of their functions and their learning process. Comparison with multi-layer perceptrons.
Some thoughts about networks with internal states. Learning approaches using such networks, overview of their dynamics.
In a magnetic field, each particle applies a force to any other particle so that all particles adjust their movements in the energetically most favorable way. This natural mechanism is copied to adjust noisy inputs in order to match their real models.
Learning vector quantization is a learning procedure with the aim to reproduce the vector training sets divided in predefined classes as good as possible by using a few representative vectors. If this has been managed, vectors which were unkown until then could easily be assigned to one of these classes.
A paradigm of unsupervised learning neural networks, which maps an input space by its fixed topology and thus independently looks for simililarities. Function, learning procedure, variations and neural gas.
An ART network in its original form shall classify binary input vectors, i.e. to assign them to a 1-out-of-n output. Simultaneously, the so far unclassified patterns shall be recognized and assigned to a new class.
In Grimm's dictionary the extinct German word „Kluster“ is described by „was dicht und dick zusammensitzet (a thick and dense group of sth.)“. In static cluster analysis, the formation of groups within point clouds is explored. Introduction of some procedures, comparison of their advantages and disadvantages. Discussion of an adaptive clustering method based on neural networks. A regional and online learnable field models from a point cloud, possibly with a lot of points, a comparatively small set of neurons being representative for the point cloud.
Discussion of an application of neural networks: A look ahead into the future of time series.
What if there were no training examples but it would nevertheless be possible to evaluate how good we have learned to solve a problem? et us regard a learning paradigm that is situated between supervised and unsupervised learning.