1. A system for machine learning comprising: a computer including a computer-readable medium having software stored thereon that, when executed by said computer, performs a method comprising the steps of being trained to learn a logistic regression match to a target class variable so to exhibit classification learning by which: an estimated error in each variable's moment in the logistic regression be modeled and reduced through constraints that require that the expected extreme error be inversely related to a t-value for that variable; an estimated error in each variable's moment in the logistic regression be modeled and reduced through constraints that require that the probability of positive and negative estimated errors be substantially equal across all variable moments; where there is substantially no bias in the probability of positive or negative estimated errors across even versus odd polynomial moments; and, an estimated error in each variable's moment in the logistic regression is constrained by a scaling that is not the sum of t-values across all variables but instead is substantially twice that sum so to reflect both positive and negative expected errors whereby when this substantially twice sum value is divided by the t-value for any variable, it yields a large expected error for small t-values and a small expected error for large t-values.