A novel approach for training a fully automatic classifier through reinforcement learning


A novel approach for training a fully automatic classifier through reinforcement learning – This paper presents two approaches to reinforcement learning (RL) in a reinforcement learning setup. The first approach is to select an active agent that does something well, and then use that agent to learn a new agent that is also good (i.e., with a good reinforcement learning reward function). When the active agent has a bad reinforcement learning reward function, RL aims at learning a good agent that is good (but not perfect) to avoid getting stuck in a bad one. We propose an RL model that is flexible, yet effective, and the reward function is adaptively encoded in the model. We evaluate our model on a large number of datasets, and show that it is significantly better than a state-of-the-art RL algorithm.

We present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.

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A novel approach for training a fully automatic classifier through reinforcement learning

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  • Diversity Driven Convolutional Auto-Encoder for Large Scale Image Registration

    Poseidon: An Efficient Convolutional Neural Network for Automatic Detection of Severe Sleep ApneaWe present a semi-supervised learning (SRL) framework to learn to classify images of patients with sleep disorders. We compare our method (called SRL-D) with one proposed semi-supervised dataset called CT-LID, which was used to train it. SRL-D has been used to train a semi-supervised classifier, but is less successful at predicting sleepiness, compared to the supervised classifier on CT-LID. We present a new approach to achieve similar performance to state-of-the-art semi-supervised classification approaches.


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