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.

This paper presents a method for object segmentation based on the combination of a visual and a textual model for the text data. It was proposed by Dharwani et al in 2009, and is still a work in progress in this paper. The proposed approach is more than 8-fold faster than the previous state-of-the-art methods without any supervision on the segmentation problem. The main contribution of this paper is to provide a solution to the problem of text segmentation using an input data set. A new method for text segmentation using this input data to improve the segmentation results (e.g., the amount of text) is also proposed. The method is evaluated using multiple test cases. The results show that the technique is competitive with other state-of-the-art hand-crafted methods.

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Tensor Logistic Regression via Denoising Random Forest

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  • Improving the performance of CNN-based image segmentation with weighted dictionary CNNs

    A Neural Projection-based Weight Normalization Scheme for Robust Video CategorizationThis paper presents a method for object segmentation based on the combination of a visual and a textual model for the text data. It was proposed by Dharwani et al in 2009, and is still a work in progress in this paper. The proposed approach is more than 8-fold faster than the previous state-of-the-art methods without any supervision on the segmentation problem. The main contribution of this paper is to provide a solution to the problem of text segmentation using an input data set. A new method for text segmentation using this input data to improve the segmentation results (e.g., the amount of text) is also proposed. The method is evaluated using multiple test cases. The results show that the technique is competitive with other state-of-the-art hand-crafted methods.


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