Titlebook: New Learning Paradigms in Soft Computing; Lakhmi C. Jain,Janusz Kacprzyk Book 2002 Springer-Verlag Berlin Heidelberg 2002 Lazy learning.ar

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978-3-7908-2499-5Springer-Verlag Berlin Heidelberg 2002

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New Learning Paradigms in Soft Computing978-3-7908-1803-1Series ISSN 1434-9922 Series E-ISSN 1860-0808

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1434-9922 achine learningLearning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of sci

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Statistical Learning by Natural Gradient Descent, an algorithm to compute the natural gradient. When the input dimension . is much larger than the number of hidden neurons, the complexity of this algorithm is of order .. It is confirmed by simulations that the natural gradient descent learning rule is not only efficient but also robust.

Armada

Reduction of Discriminant Rules Based on Frequent Item Set Calculation,s are divided in atomic operations that have been called basic steps so that it is easier to optimize the execution of any algorithm. We also present the implementation of this approach in Damisys what demonstrates that our approach is implementable and effective dealing with large datasets.

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Book 2002ing have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that hav

LIMN

Active Learning in Neural Networks,informative training sets. Such training sets can be obtained if the learner is enabled to select those training data that he or she expects to be most informative. In this case, the learner is no longer a passive recipient of information but takes an active role in the selection of the training data.

Hamper

Performance Comparisons of Neural Networks and Machine Learning Techniques: A Critical Assessment ois rather limited. This chapter presents a study based on 13 popular datasets from the UCI Machine Learning repository, which demonstrates how careful one has to be when drawing conclusions drawn from such empirical studies.