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.

The purpose of this paper is to develop a framework that enables the automated verification and classification of two commonly recognised linguistic terms in Hindi text, i.e., i-satiya and indian. In this paper, we use the phrase ‘indian lang’ to categorise i-satiya-neighbourhood as Hindi in terms of the words. In particular, we are interested in a two-way feature-vector for learning the semantic relationship between Hindi and the two languages. To this end, in a framework of a ‘dictionary of words’ we proposed an efficient method of learning the representation of i-satiya and indian. To our best knowledge, this is the first work that uses a dictionary of words as a feature vector for Hindi language as a feature vector for Hindi, irrespective of the language spoken in Hindi.

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

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  • Efficient Bayesian Learning of Determinantal Point Processes

    On Detecting Similar Languages in Text in HindiThe purpose of this paper is to develop a framework that enables the automated verification and classification of two commonly recognised linguistic terms in Hindi text, i.e., i-satiya and indian. In this paper, we use the phrase ‘indian lang’ to categorise i-satiya-neighbourhood as Hindi in terms of the words. In particular, we are interested in a two-way feature-vector for learning the semantic relationship between Hindi and the two languages. To this end, in a framework of a ‘dictionary of words’ we proposed an efficient method of learning the representation of i-satiya and indian. To our best knowledge, this is the first work that uses a dictionary of words as a feature vector for Hindi language as a feature vector for Hindi, irrespective of the language spoken in Hindi.


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