A little introduction into neural networks for supervised learning from examples

A Little Introduction into Neural Networks for Supervised Learning from Examples

Dr. Michael Schuster of ATR Interpreting Telecommunications Research Laboratories at the CLSP/JHU Summer Research Workshop on July 15, 1998 at 1:00 pm, Arellano Theater, Levering Hall. A Little Introduction into Neural Networks for Supervised Learning from Examples

- problems: (regression, classification, multi-modal regression)

- maximum likelihood estimation

- elements of neural networks

- Multi-Layer-Perceptrons (MLPs)

- Radial Basis Functions (RBFs)

- Recurrent Neural Networks (RNNs)

- Hierarchical Mixtures of Experts (HMEs)

- Objective Functions

- What does it mean to minimize mean squared error ?

- Neural network training

Neural networks are great general tools for supervised learning from examples and recently have been used for many problems. I will try to give an overview to introduce terms and concepts.

Click Here for the Presentation