Classifying data with deep learning neural records incrementally refined through expert input

A system for classifying information over a network, comprising:

• a network computer, comprising:
• a transceiver that communicates over the network;
• a memory that stores at least instructions;
• and
• a processor device that executes instructions that perform actions, including:
• providing data to a deep learning model, wherein the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data;
• classifying the data using the deep learning model and the one or more classifiers, wherein a confidence value is associated with the deep learning model classification of the data;
• and
• when a number of deep learning model classification errors exceeds a defined threshold, performing further actions, including:
• modifying one or more classifiers of the plurality of classifiers based on the data corresponding to one or more of the deep learning model classification errors;
• employing the one or more modified classifiers and that portion of the data that corresponds to the one or more deep learning model classification errors to train a fast learning model;
• employing the fast learning model and the one or more modified classifiers to also classify the data, wherein another confidence value is associated with the classification of the data by the fast learning model;
• and
• generating report information based on a comparison result of the other confidence value that is associated with the fast learning model and the confidence value that is associated with the deep learning model;
• and
• a client computer, comprising:
• a transceiver that communicates over the network;
• a memory that stores at least instructions;
• and
• a processor device that executes instructions that perform actions, including:
• providing at least a portion of the data to the deep learning model.

A processor readable non-transitory storage media that includes instructions for classifying information, wherein execution of the instructions by a processor device performs actions, comprising:

• providing data to a deep learning model, wherein the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data;
• classifying the data using the deep learning model and the one or more classifiers, wherein a confidence value is associated with the deep learning model classification of the data;
• when a number of deep learning model classification errors exceeds a defined threshold, performing further actions, including:
• modifying one or more classifiers of the plurality of classifiers based on the data corresponding to one or more of the deep learning model classification errors;
• employing the one or more modified classifiers and that portion of the data that corresponds to the one or more deep learning model classification errors to train a fast learning model;
• employing the fast learning model and the one or more modified classifiers to also classify the data, wherein another confidence value is associated with the classification of the data by the fast learning model;
• and
• generating report information based on a comparison result of the other confidence value that is associated with the fast learning model and the confidence value that is associated with the deep learning model.

A network computer for classifying information, comprising:

• a transceiver that communicates over the network;
• a memory that stores at least instructions;
• and
• a processor device that executes instructions that perform actions, including:
• providing data to a deep learning model, wherein the deep learning model was previously trained based on a plurality of classifiers and one or more sets of training data;
• classifying the data using the deep learning model and the one or more classifiers, wherein a confidence value is associated with the deep learning model classification of the data;
• when a number of deep learning model classification errors exceeds a defined threshold, performing further actions, including:
• modifying one or more classifiers of the plurality of classifiers based on the data corresponding to one or more of the deep learning model classification errors;
• employing the one or more modified classifiers and that portion of the data that corresponds to the one or more deep learning model classification errors to train a fast learning model;
• employing the fast learning model and the one or more modified classifiers to also classify the data, wherein another confidence value is associated with the classification of the data by the fast learning model;
• and
• generating report information based on a comparison result of the other confidence value that is associated with the fast learning model and the confidence value that is associated with the deep learning model.