The training dataset is further split into individual events.

Here, an atlas refers to a specific model for a population of images with parameters that are learned from a training dataset.

These fitness cases could be a set of observations or measurements concerning some problem, and they form what is called the training dataset.

Then the chromosomes are expressed as decision trees and their fitness evaluated against a training dataset.

The limitation of this method is that it relies on the training dataset to produce results.

Thus, usage of different training datasets could influence the results.

The disadvantages with lazy learning include the large space requirement to store the entire training dataset.

At this point, the data is split into two equal but mutually exclusive elements, a test and a training dataset.

The training dataset will be used to let rules evolve which match it closely.

First, a random series of "rules" are set on the training dataset, which try to generalize the data into formulas.