Material Detail

Extensive coverage of performance learning, including the Widrow-Hoff rule, backpropagation and several enhancements of backpropagation, such as the conjugate gradient and Levenberg-Marquardt variations.

Both feedforward network (including multilayer and radial basis networks) and recurrent network training are covered in detail. The text also covers Bayesian regularization and early stopping training methods, which ensure network generalization ability.

Associative and competitive networks, including feature maps and learning vector quantization, are explained with simple building blocks.

A chapter of practical training tips for function approximation, pattern recognition, clustering and prediction applications is included, along with five chapters presenting detailed real-world case studies.

Detailed examples, numerous solved problems and comprehensive demonstration software.

This will delete the comment from the database. This operation is not reversible. Are you sure you want to do it?

Thank you for reporting a broken "Go to Material" link in MERLOT to help us maintain a collection of valuable learning materials.

If you feel this material is inappropriate for the MERLOT Collection, please click SEND REPORT, and the MERLOT Team will investigate. Thank you!

You are being taken to the material on another site. This will open a new window.

Do not show me this again