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

摘要

Embodiments are directed towards classifying data using machine learning that may be incrementally refined based on expert input. Data provided to a deep learning model that may be trained based on a plurality of classifiers and sets of training data and/or testing data. If the number of classification errors exceeds a defined threshold classifiers may be modified based on data corresponding to observed classification errors. A fast learning model may be trained based on the modified classifiers, the data, and the data corresponding to the observed classification errors. And, another confidence value may be generated and associated with the classification of the data by the fast learning model. Report information may be generated based on a comparison result of the confidence value associated with the fast learning model and the confidence value associated with the deep learning model.

主权项

1. A method for classifying information over a network using a computer that includes one or more hardware processors, where each action of the method is performed by the one or more hardware processors, 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.