Machine Learning at the Crossroads of Symbolic and Connectionist Research
作者： Massimo de Francesco , Mélanie Hilario , Reynaldo Vargas , Christian Pellegrini
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摘要: Research at the University of Geneva reflects one main trends in machine learning today—the trend towards multistrategy learning. After attempts to compare a number symbolic and connectionist strategies MELANIE project, increased involvement neural network research led construction FunNet, an environment for specification simulation modular systems which heterogeneous models methods are combined accordance with established theoretical constraints. FunNet is now used as kernel software platform integrating processing. One architecture developed this extends cover these two major AI paradigms: it uses rule metalevel tool improving
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