Lars E. Holzman

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Few tools exist that address the challenges facing researchers in the Textual Data Mining (TDM) field. Some are too specific to their application, or are prototypes not suitable for general use. More general tools often are not capable of processing large volumes of data. We have created a Textual Data Mining Infrastructure (TMI) that incorporates both(More)
In this article we present a supervised learning algorithm for the discovery of finite state automata in the form of regular expressions in textual data. The automata generate languages that consist of various representations of features useful in information extraction. We have successfully applied this learning technique in the extraction of textual(More)
One of the brain's most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic(More)
In traditional models of attractor neural networks, such as the Hopfield, Boltzmann, and analog-digital ring network, inputs are available to the network either once or continuously. The intrinsic dynamics cause the network activity to converge to a " stable " state which can be a fixed point, a limit cycle, or a chaotic attractor. We suggest to incorporate(More)
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