US 2011/0299765 A1 · ROBUST PATTERN RECOGNITION SYSTEM AND METHOD USING SOCRATIC AGENTS

Abstract

A computer-implemented pattern recognition method, system and program product, the method comprising in one embodiment: creating electronically a linkage between a plurality of models within a classifier module within a pattern recognition system such that any one of said plurality of models may be selected as an active model in a recognition process; creating electronically a null hypothesis between at least one model of said plurality of linked models and at least a second model among said plurality of linked models; accumulating electronically evidence to accept or reject said null hypothesis until sufficient evidence is accumulated to reject said null hypothesis in favor of one of said plurality of linked models or until a stopping criterion is met; and transmitting at least a portion of the electronically accumulated evidence or a summary thereof to accept or reject said null hypothesis to a pattern classifier module.

Claims35

Dependent claims

A computer-implemented method of pattern recognition comprising:

obtaining classification results of a set of at least one electronic lower-level classifier modules performing pattern classification on particular input data;
using a higher-level classifier module that performs pattern classification on a pattern recognition problem different from the set of lower-level classifier modules, wherein said higher-level classifier module performs at least one of the following operations:
controlling, using one or more computers, training of the set of lower-level classifier modules based at least in part on the pattern classification task performed by the higher-level classifier module;
combining, using the one or more computers, the results of the set of lower-level classifier modules based at least in part on combining rules that vary based on the particular input data and based at least in part on the classification task performed by the higher-level classifier module, where the set of lower-level classifier modules consists of a plurality of lower-level classifier modules;
selecting, using the one or more computers, an active subset of the set of lower-level classifier modules based at least in part on a pattern classification task performed by the higher-level classifier module.

2 more elements

A computer-implemented method of sharing knowledge among a plurality of pattern classifiers, comprising:

obtaining a plurality of classifier modules including a first classifier module;
obtaining a communicable model that is either a new model or a model that has been modified by a knowledge acquisition process in the first classifier module;
transmitting, using one or more computers, said communicable model to at least one second classifier module in the plurality of classifier modules;
creating, using the one or more computers, a pair of model sets for said second classifier module in which one member of the pair of model sets is an unmodified model set for the second classifier module and one member of the pair of model sets is a modified model set that includes the communicable model;
testing, using the one or more computers, comparative performance of the pair of model sets in said at least one second classifier module;
and
making, using the one or more computers, the modified model set active in the at least one second classifier module if the modified model set performs better in said second classifier module.

4 more elements

A computer-implemented multi-stage pattern recognition method, comprising:

obtaining a sample of data to be recognized;
obtaining a plurality of labels for the given sample from a set of one or more recognition systems;
creating, using one or more computers, a set of linked model sets for at least one of the one or more recognition systems based on training said at least one recognition system on the sample of data wherein each model in the set of linked models is created by training on the given sample with a training label comprising a particular one of the plurality of labels obtained for the given sample;
obtaining a set of practice data;
testing, using the one or more computers, comparative performance of the linked model sets on the practice data;
correcting, using the one or more computers, the label on the given data sample to agree with the label associated with model from the linked set of models that performs best in the comparative performance testing on the practice data;
and
returning, using the one or more computers, a corrected the label as corrected as the final recognition result of the multi-stage recognition process.

5 more elements

Dependencies are inferred from the claim text.

Robust pattern recognition system and method using socratic agents

Life of this patent

Robust pattern recognition system and method using socratic agents

Life of this patent

Abstract

Claims35