Learning Deep Belief Networks from Non-stationary Streams

  title={Learning Deep Belief Networks from Non-stationary Streams},
  author={Roberto Calandra and Tapani Raiko and Marc Peter Deisenroth and Federico Montesino-Pouzols},
Deep learning has proven to be beneficial for complex tasks such as classifying images. However, this approach has been mostly applied to static datasets. The analysis of non-stationary (e.g., concept drift) streams of data involves specific issues connected with the temporal and changing nature of the data. In this paper, we propose a proofof-concept method, called Adaptive Deep Belief Networks, of how deep learning can be generalized to learn online from changing streams of data. We do so by… CONTINUE READING
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