Neural Networks
A neural network is an interconnected assembly of simple processing elements, units
or nodes whose functionality is loosely based on the animal neuron. The processing ability
of the network is stored in the inter-unit connection strengths, or weights, obtained by a
process of adaption to, or learning from a set of training patterns.
The artificial equivalents of biological neurons are the nodes or units. Synapses are modeled
by a single number or weight so that each input is multiplied by a weight. The weighted signals
are summed together to supply a node activation. The activation is then compared with a threshold;
if the activation exceeds the threshold, the unit produces a high-valued output, otherwise it
outputs zero.
Neural Networks are often used for statistical analysis, classification and data modeling.
Limitations of Neural Networks
Neural Networks are algorithmic systems which use historical data to identify trends, clusters,
and patterns. Unsupervised neural networks (clustering) are inefficient and inadequate. Supervised
learning are limited by their training, i.e. they can reliably recognize only the kind of information
on which they were trained.
Brighterion's Neural Networks Library
iPrevent's proprietary Neural Networks technology can translate any database to neurons without user
intervention and has significantly accelerated the speed of convergence as compared to typical Neural
Networks algorithms such as Back Propagation. iPrevent's Neural Networks are incremental and adaptive,
allowing the size of the output classes to change dynamically. Additionally, in its expert mode, iPrevent
provides a library of twelve different Neural Networks models for use in customization.
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