Tensor Logistic Regression via Denoising Random Forest


Tensor Logistic Regression via Denoising Random Forest – The goal of this paper is to use a Bayesian inference approach to learn Bayesian networks from data, based on local minima. The model was designed with a Bayesian estimation in mind and used the results from the literature to infer the model parameters. We evaluate the hypothesis on two datasets, MNIST and Penn Treebank. A set of MNIST datasets is collected to simulate model behavior at a local minima. The MNIST dataset (approximately 1.5 million MNIST digits) is used as a reference. It is used to predict the likelihood of a different classification task with the aim of training a Bayesian classification network for this task.

We propose a new stochastic optimization algorithm for sparsely sampled optimization (SMOG). In SMOG, an optimizer can choose an unaligned function which performs exactly the same as the data and then performs the optimizer’s best possible estimation. We further show that optimization for this optimization is tractable, and it can be efficiently formulated as the optimal choice of optimization metric between the data and its metric. We show that the choice of optimization metric is tractable, but computationally hard. Furthermore, we give a new efficient algorithm for optimizing the optimal optimization metric, which can be applied to any SMOG task with a single SMOG instance. We evaluate our algorithm on several benchmark datasets, and report the performance of our algorithm.

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Tensor Logistic Regression via Denoising Random Forest

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  • A Probabilistic Latent Non-Monotonic Programming Model for Data Representation: A Latent Variable Approach

    Stochastic Dual Coordinate Optimization with Side InformationWe propose a new stochastic optimization algorithm for sparsely sampled optimization (SMOG). In SMOG, an optimizer can choose an unaligned function which performs exactly the same as the data and then performs the optimizer’s best possible estimation. We further show that optimization for this optimization is tractable, and it can be efficiently formulated as the optimal choice of optimization metric between the data and its metric. We show that the choice of optimization metric is tractable, but computationally hard. Furthermore, we give a new efficient algorithm for optimizing the optimal optimization metric, which can be applied to any SMOG task with a single SMOG instance. We evaluate our algorithm on several benchmark datasets, and report the performance of our algorithm.


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