The solution to data mining problems often involves discovering non-linear relationships in large, noisy datasets.
Perception involves discovering the cues that consistently signal things of interest and distinguishing them from noise.
In general, the approach involves a machine-learning algorithm for discovering associations between point-occurrence data and sets of electronic maps summarizing environmental/ecologic dimensions that may or may not be important in limiting species' geographic distributions.
This solution approach involves modeling modular products, formulating their design knowledge, and discovering implicit knowledge into design rules based on the rough set theory.
This qualitative approach involves moving back and forth in the data to discover common patterns and topics.
One such approach involves "progressive neural networks".
The European approach involves chopping the tree down.
A third approach involves the natural chemicals known as pheromones.
The approach involves three steps.
The first approach involves blocking farnesylation.
