On Generalized Stochastic Optimization and Bayes Function Minimization


On Generalized Stochastic Optimization and Bayes Function Minimization – The problem of generalized linear programming is addressed by the stochastic gradient descent method. The stochastic gradient method is characterized by its linear convergence rate and a constant convergence rate. A regularization term is also provided in this framework. Experimental results show that this regularization allows the stochastic gradient method to approximate the Bayesian optimisation problem.

In this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.

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On Generalized Stochastic Optimization and Bayes Function Minimization

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  • Towards Effective Deep-Learning Datasets for Autonomous Problem Solving

    Pervasive Sparsity Modeling for Compressed Image AcquisitionIn this paper, we propose an ensemble-based image clustering method based on joint sparse-Gaussian models (SGRMs). The main idea is to learn the ensemble size that is a function of the number of subspaces within the ensemble. The goal in the proposed SGRM is to partition the ensemble in a random manner, which is based on a set of randomly selected clusters. We compare the proposed methods to methods that perform multiple time-scale clustering simultaneously. The experimental results show that the proposed method outperforms existing methods and comparable methods.


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