A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis


A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis – Multi-dimensional multi-valued Markov models have recently gained increasing interest in the predictive performance of various machine learning applications. We propose a new multi-dimensional method for multi-way learning based on the convex relaxation of the Markov Bayes matrix. This method uses a Gaussian model to minimize the regret of the least squares distribution of the posterior distribution matrix. The prior distribution matrix of the posterior distribution matrix is then used as an input for the model to derive the Markovian covariance matrix. This covariance matrix is used as covariance matrix for the linear regression problem. To solve the linear regression problem, we propose a new algorithm which performs better than the state-of-the-art. The proposed method is able to generalize well in a variety of domains such as structured decision making. The proposed method is fast and robust to the non-linearity of the Markovian covariance matrix and the existence of outliers.

The large discrepancy between the medical accuracy and the practical performance of human clinicians has become evident. In medical computer therapy, human practitioners need to assess patient outcomes for their patients, but rarely do real-time clinicians consider and use patient-relevant data. In this work, a deep learning based approach to automatic data stream analysis is presented. We study the task of estimating the performance of a human practitioner in assessing patients, and show that such a process can produce a useful information about patients’ outcomes, even for very short time horizons. In particular, human practitioner error rates are reduced from 90% and 80% to 5% and 7% respectively, in a real-world scenario.

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A Comparative Study of Threshold Based Methods for Multiplicative Data Analysis

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  • Towards a Framework of Deep Neural Networks for Unconstrained Large Scale Dataset Design

    A Comparative Analysis of the Two Expert-Grade Classification Algorithms for fMRI-based Brain Tissue ClassificationThe large discrepancy between the medical accuracy and the practical performance of human clinicians has become evident. In medical computer therapy, human practitioners need to assess patient outcomes for their patients, but rarely do real-time clinicians consider and use patient-relevant data. In this work, a deep learning based approach to automatic data stream analysis is presented. We study the task of estimating the performance of a human practitioner in assessing patients, and show that such a process can produce a useful information about patients’ outcomes, even for very short time horizons. In particular, human practitioner error rates are reduced from 90% and 80% to 5% and 7% respectively, in a real-world scenario.


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