Weighscore
Neural Network Toolkit

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The neural network technologies are the way to cut the number of the manual operations, speed up the processing of data, and cut the costs on the installation of a decision-making system. In the financial area, they are used for checking whether an operation is fraudulent, evaluating the probability of the default, evaluating risks in the insurance, and various financial anticipation. Neural networks are also used in other fields of business for many types of events prediction like elecrticity supply networks overloads or social groups behaviour. In the development of our Neural Network Toolkit, we have taken into account our previous system integration experience and implemented the Toolkit with the advantages of the client-server architecture and the web services.

Product Features

  • Free workstation command line tool for the initial neural network training for the server and for educational or scientific purposes;
  • platform independent, written in java - it may be run on Solaris, Windows, Linux etc;
  • uses JDBC data source as a training case set - it may get training data from any source if there is a JDBC driver for it;
  • converts (translates) the simple string reference entries to neurons' numeric values - it requires minimum preliminary data transformation;
  • stores the neural network in a simple XML file - editable with any text editor.

    Benefits of the Server Version

    The main advantage to Weighscore Neural Network Server is that is has the ability to be trained online, simultaneously with its querying. When new data that characterizes the response that was given earlier arrives, the network may be slightly updated using the new data without stopping.

    Moreover, there are the following advantages:

  • thread safe simultaneous processing - suitable for distributed calling;
  • easily called with a simple protocol - see examples for java and winsock in the reference manual;
  • callable as an Axis webservice - easy to call from almost any client platform.

    See the product documentation for more technical information



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