Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many connectionist learning models are implemented using a gradient descent in a least squares error function of the output and teacher signal. The present model generalizes, in particular, back-propagation [1] by using Minkowski-r por metrics. For small r's a "city-block" error metric is approximated and for large r's the "maximum" or "supremum" metric is approached. while for r=2 the standard back-propagation model results. An implementation of Minkowski-r back-propagation is described, and several experiments are done which show that different values of r may be desirable for various purposes. Different r values may be appropriate for the reduction of the effects of outliers (noise), modeling the input space with more compact clusters, or modeling the statistics of a particular domain more naturally or in a way that may be more perceptually or psychologically meaningful (e.g. speech or vision).
['Stephen José Hanson', 'David J. Burr']
I am writing a research paper on a idea Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals
[{"from": "human", "value": "I am writing a research paper on a idea Minkowski-r Back-Propagation: Learning in Connectionist Models with Non-Euclidian Error Signals"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Many connectionist learning models are implemented using a gradient descent in a least squares error function of the output and teacher signal. The present model generalizes, in particular, back-propagation [1] by using Minkowski-r por metrics. For small r's a \"city-block\" error metric is approximated and for large r's the \"maximum\" or \"supremum\" metric is approached. while for r=2 the standard back-propagation model results. An implementation of Minkowski-r back-propagation is described, and several experiments are done which show that different values of r may be desirable for various purposes. Different r values may be appropriate for the reduction of the effects of outliers (noise), modeling the input space with more compact clusters, or modeling the statistics of a particular domain more naturally or in a way that may be more perceptually or psychologically meaningful (e.g. speech or vision)."}]