On the Computation of Stochastic Models: The Naive Bayes Machine Learning Approach


On the Computation of Stochastic Models: The Naive Bayes Machine Learning Approach – In this paper, the first-order random-value stochastic gradient descent (RGGSD) is proposed. We first learn the gradient of the sample distribution over the sampled space into a Gaussian vector. Then, we propose a stochastic gradient descent technique, that learns the stochastic gradient of the sample distribution for the gradient of Gaussian variables. In the proposed method, the gradient of the sample distribution can be directly transferred, by the gradient of the gradient of the sample distribution, to a random distribution over Gaussian samples. The proposed method enables an efficient method of the stochastic gradient descent (SGD) by providing a set of Gaussian distributions which is more compact than the sample distribution of the data distribution. The experimental results showed that the proposed method is successful both on the synthetic data and the real data. The proposed method is compared with state-of-the-art stochastic gradient descent (SGD) methods.

This work presents two approaches for solving the stochastic optimization problem. This solution is a general representation of the convex optimization problem, and is used to solve the recently-proposed SVM. The problem has been studied extensively, and the results of this study can be compared with previous work.

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On the Computation of Stochastic Models: The Naive Bayes Machine Learning Approach

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    Convex Penalized Kernel SVMThis work presents two approaches for solving the stochastic optimization problem. This solution is a general representation of the convex optimization problem, and is used to solve the recently-proposed SVM. The problem has been studied extensively, and the results of this study can be compared with previous work.


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