Abstract: Embodiments herein achieve a machine-learning system for managing shuffling of input training datasets. The machine-learning system includes a training dataset manager configured to shuffle an input dataset received from each of a plurality of electronic devices. Further, the training dataset manager is configured to split the input training datasets into a plurality of mini-batches. Each of the mini-batches, along with the target values, defines an error surface corresponding to an error function. A learning manager is configured to obtain a cross mini-batch discriminator based on the error function for each of the mini-batches. Further, the learning manager is configured to select a mini-batch associated with a least cross mini-batch discriminator from the plurality of mini-batches as optimal mini-batch.

Abstract: Embodiments herein achieve a machine-learning system for managing shuffling of input training datasets. The machine-learning system includes a training dataset manager configured to shuffle an input dataset received from each of a plurality of electronic devices. Further, the training dataset manager is configured to split the input training datasets into a plurality of mini-batches. Each of the mini-batches, along with the target values, defines an error surface corresponding to an error function. A learning manager is configured to obtain a cross mini-batch discriminator based on the error function for each of the mini-batches. Further, the learning manager is configured to select a mini-batch associated with a least cross mini-batch discriminator from the plurality of mini-batches as optimal mini-batch.

Abstract: This disclosure includes various embodiments of apparatuses, systems, and methods for adaptive batch mode active learning for evolving a classifier. A corpus of unlabeled data elements to be classified is received, a batch size is determined based on a score function, a batch of unlabeled data elements having the determined batch size is selected from the corpus and labeled using a labeling agent or oracle, a classifier is retrained with the labeled data elements, these steps are repeated until a stop criterion has been met, for example, the classifier obtains a desired performance on unlabeled data elements in the corpus. The batch size determination and selection of a batch unlabeled data elements may be based on a single score function. The data elements may be video, image, audio, web text, and/or other data elements.