发明名称 Exponential priors for maximum entropy models
摘要 The subject invention provides for systems and methods that facilitate optimizing one or mores sets of training data by utilizing an Exponential distribution as the prior on one or more parameters in connection with a maximum entropy (maxent) model to mitigate overfitting. Maxent is also known as logistic regression. More specifically, the systems and methods can facilitate optimizing probabilities that are assigned to the training data for later use in machine learning processes, for example. In practice, training data can be assigned their respective weights and then a probability distribution can be assigned to those weights.
申请公布号 US2005165580(A1) 申请公布日期 2005.07.28
申请号 US20040766348 申请日期 2004.01.28
申请人 GOODMAN JOSHUA T. 发明人 GOODMAN JOSHUA T.
分类号 G06F15/00;G06F17/10;G06F17/18;G06K9/62;(IPC1-7):G06F17/18 主分类号 G06F15/00
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