Within a very short time we will have the current body of astronomical knowledge available for computer processing. In addition to improved access and timely availability electronic publications have the advantage of being searchable. Text Data Mining Using Neural Networks Artificial neural networks are well suited for tasks that are characterized by noise, poorly understood intrinsic structure, and changing characteristics, each of which are present when dealing with text. Learning techniques are favorable in such an environment compared to algorithmic and knowledge-based approaches. Input patterns presented to the neural network will propagate selectively through it, depending on the different weights of the different connections.
Neural networks are inherently parallel computational models.
The unit with the lowest activation is referred to as the winner , c , of the training iteration, i.e., . Finally, the weight vector of the winner as well as the weight vectors of selected units in the vicinity of the winner are adapted. This adaptation is implemented as a gradual reduction of the difference between input pattern and weight vector, i.e., . Geometrically speaking, the weight vectors of the adapted units are moved a bit towards the input pattern. The number of units that are affected by adaptation is determined by a so-called neighborhood function, h ci .