Projects / Joone

Joone

Joone (Java Object Oriented Neural Engine) is an artificial neural network Java framework. It is used to build and train neural networks with a powerful visual environment. It has a modular design and can be easily extended by writing new modules to implement new learning algorithms or architectures.

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Recent releases

  •  19 Jan 2007 00:41

    Release Notes: Performance was improved by 50%, thanks to heavy refactoring of the core engine, by adding the single-thread mode. A set of tools was added in order to hide the complexity of the API. A new Image I/O component has been added in order read and write directly from/to image files. A new SoftMax Layer has been added to build neural networks able to resolve 1 of C classification problems. The usability of the property panels has been improved by adopting a new file chooser panel and a visual calendar panel. Several bugs were fixed and the documentation has been updated with the new features.

    •  09 Sep 2005 14:53

      Release Notes: This release adds support for the Groovy scripting language. LogarithmicPlugin has been added, to apply a logarithmic transformation (base e) to input data. "Save as XML" has been added to the GUI Editor, in order to permit saving a neural network in XML format. A number of bugs have been fixed, including a problem that prevented SangerSynapse from learning when in training mode. The inspection panel no longer shows the biases for Layers for which this doesn't make sense. This release fixes the lack of the first column when the inspected values were copied in the clipboard.

      •  06 Feb 2005 21:23

        Release Notes: This release adds many new features, including the Radial Basis Function, a new Input Connector useful to share the same data source between several layers, a new weights' initializing mechanism, new learning algorithms, etc. A lot of bugs have been fixed and the documentation has been improved.

        •  20 Sep 2004 15:24

          Release Notes: The Distributed Training Environment (DTE) has been completely rewritten. The core framework now has a modular architecture based on plugins, making it easily expandable. The DTE is easily configurable in order to define any complex global optimization technique by using a simple and powerful parameter file written in XML. The new DTE is compliant with the latest Sun Jini 2.0.

          •  14 Sep 2004 11:14

            Release Notes: This version fixes the 'No matching patterns' problem. The RMSE is not set to 0.0 when a network is deserialized. A NullPointerException was fixed in the YahooFinanceInputSynapse when no input data is present. The BatchLearner has been fixed in order to use the momentum parameter.

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