Automated Accounting Systems
Neural networks are already being used for financial applications such as evaluating credit worthiness and predicting stock price movements. Accountbot.com is a development-phase site for use in the application of advanced computing techniques to the fields of finance and accounting. Artificial neural networks have been used in the domain of finance to try to predict stock prices. The stock need is a very complicated system which may have patterns that humans may not recognize. Neural networks are much better at solving configuration recognition problems than are computers that use common, sequential data processing. Networks have additionally been used for market study and files validation products and services.
With unsupervised knowledge, a neural network is given data, but not desired outputs. The network itself must make decisions on how to group the files and draw interferences. Its simulated neural structure must self-organize information and discern its patterns. The dynamics of unsupervised knowledge are not well understood and the majority of neural networks are trained with supervision.
In statistical analysis, if you have too many variables in an experimental design relative to the number of observations for which you are estimating the simulation, you may wind up "over fitting" the experimental design to the files. The model becomes very accurate for the data in question, but is too specific to that information and does not work well for other information. Another way of looking at this situation is that the prototype treats links as if they are causal when they are really just random; it sees patterns that are not really there. Artificial Neural Networks (ANNs) will generally fall prey to the same mistake when they are trained on too few cases relative to the complexity of the neural simulation. It is a tradeoff. A network with too a large number of "moving parts" might over-fit the practice files and not work well for different data. A network with too few "moving parts" may not be high performance and flexible the right amount of to discern important patterns in the files.
Neural networks originated in laboratories and academic settings, but are increasingly being applied to real world problems. Neural networks are particularly useful for problems in which there is a causal relationship, even if complex and non-linear, between a set of input variables and an output variable. There are most such problems in the subjects of: finance; medical practice; positioning; and connection between humans and computers (eg. voice and handwriting).
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