Search Results for "java swing calendar"
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Showing 9 open source projects for "java swing calendar"
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Neuroph is lightweight Java Neural Network Framework which can be used to develop common neural network architectures. Small number of basic classes which correspond to basic NN concepts, and GUI editor makes it easy to learn and use.
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Program to performing the complete cycle of neural networks analysis: preparing data, choosing neural network (CasCor, MP, LogRegression, PNN), learning of network, monitoring learning state, ROC-analysis, optimization of network parameters using GA.
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Neuroph OCR - Handwriting Recognition is developed to recognize hand written letter and characters. It's engine derived's from the Java Neural Network Framework - Neuroph and as such it can be used as a standalone project or a Neuroph plug in.
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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This project is aimed at making a complete flexible Neural network library in Java. The library works fine with most complex problems.Future targets: Configurable Learning rate and momentum. More details at http://www.w3avatar.com
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TCR Neuroph - Text Character Recognition is java tool developed to recognize scanned text , using Java Neural Network Framework - Neuroph
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JAFF: Just Another Financial Framework. Technical Analysis (studying the price and trading history) of some stock quotes from internet. Neural Network analysis for non-linear prediction and forecasting.
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The aim of GUINNEA (Graphical User Interfaced Neural Network Architecture) is to develop a comfortable and high-featured neural net simulator which is highly configurable and flexible. It will support many neural nets and visualization features for those
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LiNNS is not just a Neural Network Simulator, but a Neural Network System - a framework which covers the full lifecycle of a neural network, from design and research till usage in an external application.
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