Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.

Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.

Abstract: A previously trained classification model associated with the machine learning system is configured to process an input to generate i) a first prediction that represents a characteristic associated with the input, and ii) a representation of accuracy associated with the prediction. A retraining subsystem is configured to receive the input, the first prediction, and the representation of accuracy. The retraining subsystem processes the input to generate a prediction representing a characteristic. A sufficiency of certainty of the first prediction is determined based on at least the input, the first prediction, the measure of accuracy, and the second prediction. Based at least on the determined sufficiency the retraining subsystem causes the machine learning system to be automatically retrained, be retrained using the input with active learning or not retrained.

Abstract: Systems, methods, and media for optical recognition are provided. In some embodiments, systems for optical recognition are provided, the systems comprising: at least one hardware processor that: identifies a plurality of fixation points in optically detected data; identifies features of the plurality of fixation points; and identifies one or more characteristics of an object represented in the optically detected data. In some embodiments, methods for optical recognition are provided, the methods comprising: identifying a plurality of fixation points in optically detected data using a hardware processor; identifying features of the plurality of fixation points using the hardware processor; and identifying one or more characteristics of an object represented in the optically detected data using the hardware processor.

Abstract: Systems, methods, and media for optical recognition are provided. In some embodiments, systems for optical recognition are provided, the systems comprising: at least one hardware processor that: identifies a plurality of fixation points in optically detected data; identifies features of the plurality of fixation points; and identifies one or more characteristics of an object represented in the optically detected data. In some embodiments, methods for optical recognition are provided, the methods comprising: identifying a plurality of fixation points in optically detected data using a hardware processor; identifying features of the plurality of fixation points using the hardware processor; and identifying one or more characteristics of an object represented in the optically detected data using the hardware processor.