Practical Applications of Data Mining

230 Chapter 6 Neural Networks

Linear Associative Memory (LAM)•

Optimal Linear Associative Memory (OLAM)•

Sparse Distributed Associative Memory (SDM)•

Fuzzy Associative Memory (FAM)•

Counterpropogation Network (CPN)•

6.3.2 Supervised Learning Models

In contrast to the unsupervised learning model, the supervised learning process

requires that target values be provided. From a training dataset the input vector

will generate the rules according to the desired output by adjusting the weights.

The weights are used for evaluating the input test data. The desired output will

be provided to the net, and then the weights are adjusted to fit the model to the

desired goal. If the desired goal is not met, the learning process will continue

to iterate ...