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Off the Beaten path – Using Deep Forests to Outperform CNNs and RNNs

Summary:  How about a deep learning technique based on decision trees that outperforms CNNs and RNNs, runs on your ordinary desktop, and trains with relatively small datasets.  This could be a major disruptor for AI.

Suppose I told you that there is an algorithm that regularly beats the performance of CNNs and RNNs at image and text classification.

  • That requires only a fraction of the training data.
  • That you can run on your desktop CPU device without need for GPUs.
  • That trains just as rapidly and in many cases even more rapidly and lends itself to distributed processing.
  • That has far fewer hyperparameters and performs well on the default settings.
  • And relies on easily understood random forests instead of completely opaque deep neural nets.

Well there is one just announced by researchers Zhi-Hua Zhou and Ji Feng of the National Key Lab for Novel Software Technology, Nanjing University, Nanjing, China.  And it’s called gcForest.

Read the full article here

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