Sparse Bayesian Online Convex Optimization with Linear and Nonlinear Loss Functions for Statistical Modeling


Sparse Bayesian Online Convex Optimization with Linear and Nonlinear Loss Functions for Statistical Modeling – This paper proposes a new approach for linear and nonlinear prediction of multi-dimensional feature vectors based on Gaussian Processes (GP). This approach is based on the idea of learning a mixture of functions in a regression framework that captures both the number of variables (variable, feature) and the model parameters (features). A priori, the mixture of functions is derived from the covariance matrix of the features. This mixture is used as a surrogate for the regression function. The proposed algorithm is tested directly on the MNIST dataset (MNIST-2) by solving a Gaussian Process (GP) to estimate the covariance matrix. The approach is evaluated on the MNIST dataset and the proposed approach is compared using both simulated and real data sets. The results show that the proposed algorithm outperforms the state-of-the-art GP models, with the proposed method having the same accuracy rate as the traditional one for real data.

Image classification is a challenging problem due to the wide variation of images used in many image processing applications. In each particular problem, researchers have to make use of various techniques such as supervised learning, multilevel learning, and machine learning. The problem is usually characterized by one of two major characteristics: a) image quality is highly variable, and b) it is difficult to estimate the image quality in terms of the true class labels. Therefore, a novel approach is to combine a supervised and a supervised image classification to gain a better and better classification performance. In this paper, we propose and evaluate an unsupervised Deep Reinforcement Learning (DRL) method which combines a supervised and a supervised image classification with a reinforcement learning (RL) method: (1) the RL method learns a model of an image, and (2) the RL method can learn a high-dimensional representation of the image with more accuracy than the supervised model, by training the RL model to classify it. We demonstrate our method on the ILSVRC 2017 and ILSVRC 2012 benchmark datasets.

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Sparse Bayesian Online Convex Optimization with Linear and Nonlinear Loss Functions for Statistical Modeling

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  • Towards a Unified Model of Knowledge Acquisition and Linking

    Towards a unified view on image quality assessmentImage classification is a challenging problem due to the wide variation of images used in many image processing applications. In each particular problem, researchers have to make use of various techniques such as supervised learning, multilevel learning, and machine learning. The problem is usually characterized by one of two major characteristics: a) image quality is highly variable, and b) it is difficult to estimate the image quality in terms of the true class labels. Therefore, a novel approach is to combine a supervised and a supervised image classification to gain a better and better classification performance. In this paper, we propose and evaluate an unsupervised Deep Reinforcement Learning (DRL) method which combines a supervised and a supervised image classification with a reinforcement learning (RL) method: (1) the RL method learns a model of an image, and (2) the RL method can learn a high-dimensional representation of the image with more accuracy than the supervised model, by training the RL model to classify it. We demonstrate our method on the ILSVRC 2017 and ILSVRC 2012 benchmark datasets.


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