There have been several recent studies concerning feedforward networks and the problem
of approximating arbitrary functionals of a finite number of real variables. Some of these …

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The problem of the approximation of nonlinear map**,(especially continuous map**s) is
considered. Regularization theory and a theoretical framework for approximation (based on …

Instance-based learning techniques typically handle continuous and linear input values
well, but often do not handle nominal input attributes appropriately. The Value Difference …

Instance-based learning algorithms are often faced with the problem of deciding which
instances to store for use during generalization. Storing too many instances can result in …

" Most neural network programs for personal computers simply control a set of fixed, canned
network-layer algorithms with pulldown menus. This new tutorial offers hands-on neural …

The author extends a previous review and focuses on feed-forward neural-net classifiers for
static patterns with continuous-valued inputs. He provides a taxonomy of neural-net …

Learning an input-output map** from a set of examples, of the type that many neural
networks have been constructed to perform, can be regarded as synthesizing an …

Composed of three sections, this book presents the most popular training algorithm for
neural networks: backpropagation. The first section presents the theory and principles …

A key task for connectionist research is the development and analysis of learning algorithms.
This paper examines several supervised learning algorithms for single-cell and network …