US 2016/0110657 A1 · Configurable Machine Learning Method Selection and Parameter Optimization System and Method

Abstract

A system and method for selecting a machine learning method and optimizing the parameters that control its behavior including receiving data; determining, using one or more processors, a first candidate machine learning method; tuning, using one or more processors, one or more parameters of the first candidate machine learning method; determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition; and outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

Claims20

Dependent claims

A method comprising:

receiving data;
determining, using one or more processors, a first candidate machine learning method;
tuning, using one or more processors, one or more parameters of the first candidate machine learning method;
determining, using one or more processors, that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition;
and
outputting, using one or more processors, the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

3 more elements

A system comprising:

one or more processors;
and
a memory storing instructions that, when executed by the one or more processors, cause the system to:
receive data;
determine a first candidate machine learning method;
tune one or more parameters of the first candidate machine learning method;
determine that the first candidate machine learning method and a first parameter configuration for the first candidate machine learning method are the best based on a measure of fitness subsequent to satisfaction of a stop condition;
and
output the first candidate machine learning method and the first parameter configuration for the first candidate machine learning method.

6 more elements

Dependencies are inferred from the claim text.

Configurable Machine Learning Method Selection and Parameter Optimization System and Method

Life of this patent

Configurable Machine Learning Method Selection and Parameter Optimization System and Method

Life of this patent

Abstract

Claims20