Notes

Machine learning is a method of data analysis that automates analytical model building.
It is a branch of artificial intelligence based on the idea that systems can learn from data,
identify patterns and make decisions with minimal human intervention.

Machine learning - automated model building
Automated model building that is learned from data
Most supervised learning algorithms for Artificial Neural Networks (ANN)aim at minimizing the sum of the squared error of the training data [12, 11, 5, 10]. It is well known that learning algorithms that are based only on error minimization do not guarantee good generalization performance models. In addition to the training set error, some other network-related parameters should be adapted in the learning phase in order to control generalization performance. The need for more than a single objective function paves the way for treating the supervised learning problem with multi-objective optimization techniques. Although the learning problem is multi-objective by nature, only recently it has been given a formal multi-objective optimization treatment [16]. The problem has been treated from different points of view along the last two decades.