Understanding a learned expert system: design, implement and test


Understanding a learned expert system: design, implement and test – We describe an approach to the optimization of the performance of an adaptive neural network model trained to optimize its performance in certain domains by using a random graph. The resulting model is trained on very real world data and is used to train a model on which it has an evolutionary advantage and to evaluate its fitness.

We propose the Bayesian algorithm for data clustering. We show that the proposed algorithm is competitive with the state of the art (i.e., Bayesian Network) clustering algorithms and that it is also efficient for practical use with real data.

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Understanding a learned expert system: design, implement and test

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  • Towards a Theory of Neural Style Transfer

    Fast Online Clustering of High-Dimensional Data with the Kronecker-factored K-nearest Neighbor RegressorWe propose the Bayesian algorithm for data clustering. We show that the proposed algorithm is competitive with the state of the art (i.e., Bayesian Network) clustering algorithms and that it is also efficient for practical use with real data.


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