ProEval: A Risk-Agnostic Decision Support System


ProEval: A Risk-Agnostic Decision Support System – We present an automated strategy for a new game, where you are the main character in a campaign of a human-robot team. We show that the system, named AIXG, is capable of predicting the outcome of the campaign, and that it can be used to help humans in the campaign in a very powerful way. Our system is based on an optimization algorithm based on the minimax method for the cost function and an online version of the max-product strategy which was used to improve the minimax and max-product strategies. We show that in some situations our algorithm can be more effective than the minimax method and is much more powerful than max-product.

Learning a large class of discriminative features with high similarity is considered. This paper aims at improving the performance of many-class classification algorithm with high similarity. The problem is first addressed by means of neural network. A recurrent neural network model is constructed for a given classification task. The model is trained using a set of discriminative features which are drawn from the set of discriminative features at each training step. In order to extract features related with the task, the model has to learn a set of different discriminative features at each training step. As well as this, a Bayesian network is used to jointly learn the discriminative features at each step. Then the model can learn discriminative features with high similarity to obtain a lower bound value of the bound of the similarity measure. Experimental results on the MNIST dataset show that the proposed method improves classification performance compared to other state-of-the-art deep-learning-based discriminative models.

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ProEval: A Risk-Agnostic Decision Support System

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  • Recurrent Neural Models for Autonomous Driving

    Optimal cost for error: a deterministic outcome functionLearning a large class of discriminative features with high similarity is considered. This paper aims at improving the performance of many-class classification algorithm with high similarity. The problem is first addressed by means of neural network. A recurrent neural network model is constructed for a given classification task. The model is trained using a set of discriminative features which are drawn from the set of discriminative features at each training step. In order to extract features related with the task, the model has to learn a set of different discriminative features at each training step. As well as this, a Bayesian network is used to jointly learn the discriminative features at each step. Then the model can learn discriminative features with high similarity to obtain a lower bound value of the bound of the similarity measure. Experimental results on the MNIST dataset show that the proposed method improves classification performance compared to other state-of-the-art deep-learning-based discriminative models.


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