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Titlebook: Biological and Artificial Intelligence Environments; Bruno Apolloni,Maria Marinaro,Roberto Tagliaferri Conference proceedings 2005 Springe

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Neural Classification of HEP Experimental Data traditional MLP. Test error below 25% is archived by all architectures in two different simulation strategies. EαNet performance are 1 to 2%better on test error with respect to the other two architectures using the smaller network topology. The design of a digital implementation of the proposed neural network is also outlined.
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https://doi.org/10.1007/978-90-313-9437-1 to an agreement and a disagreement between a pair of genes. We also intend to validate the role of the correlation clustering algorithm by comparing the results with a support vectors clustering approach [Ben-Hur et al., 2001] that is demonstrated to perform well for many applications.
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,Friedhofsschäden und Vandalismus, is trained over a segment of the signal the classification task is completed in a time interval significantly shorter than the time-window used for the training. Stimuli composed by many complex signals are recognized and classified even if some signals are absent.
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Stratistical Learning for Parton Identificationaltively easy to distinguish between jets originating from gluons and those originating from quarks in an energy-independent manner. Distinguishing between quark flavours is more difficult and will require inclusion of other variables.
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https://doi.org/10.1007/978-3-7091-9911-4bracing the Generative Topographic Mapping as a special case. This article describes the use of PPS for the analysis of yeast gene expression levels from microarray chips showing its effectiveness for high-D data visualization and clustering.
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