Unsupervised Active Learning with Partial Learning


Unsupervised Active Learning with Partial Learning – A method for automatically learning to perform intelligent actions from video by optimizing the model-free training data of a given task is presented. Using a novel and fast learning algorithm, we show that a modified version of the KNN-based algorithm — K-Net — learns to perform the task effectively in a given environment, achieving state-of-the-art performance on the K-NN task when trained using only minimal data. We also show how the updated version can be used to learn to learn to perform this task effectively by directly optimizing the input data.

We extend the hand-crafted text classification problem to embedding text. In this work, we first embed the text into a hierarchical structure as a recurrent network. Then the embeddings are evaluated on a set of sentences and a corpus with more than 1000 sentences. We show how to incorporate the embedding into the learning algorithm. We demonstrate that by comparing well the classification accuracies of the encoder and encoder, the encoder performs significantly better than the encoder in terms of recognition rate and accuracy.

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Unsupervised Active Learning with Partial Learning

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