United States Patent
US Patent 8032473: Generalized reduced error logistic regression…
US 8032473 · granted 2011-10-04
Abstract
A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.
| Patent Number | 8032473 |
|---|---|
| Title | Generalized reduced error logistic regression method |
| Filed | 2008-07-31 |
| Granted | 2011-10-04 |
| Inventor(s) | Rice; Daniel M. |
| CPC Classification | G06F 17/00, G06N 5/02 |
| Number of Claims | 35 |
Abstract
A machine classification learning method titled Generalized Reduced Error Logistic Regression (RELR) is presented. The method overcomes significant limitations in prior art logistic regression and other machine classification learning methods. The method is applicable to all current applications of logistic regression, but has significantly greater accuracy using smaller sample sizes and larger numbers of input variables than other machine classification learning methods including prior art logistic regression.
Claim 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 andreduced 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 requirethat 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 polynomialmoments; 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 negativeexpected errors whereby when this subst
Claims
35 totalA 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 andreduced 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 requirethat 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 polynomialmoments; 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 negativeexpected errors whereby when this subst