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Keywords: Open Distributed Processing; Computational Viewpoint; Quality of ..... b CD)0 A 17I Y8F 1 B 5 7I 0 2×022×5 × G ...

7. The Discrete Matrix Model for Sparse Gaussian Processes. Abstract: A typical application of sparse matrix processes (SMS) is the analysis and classification of data sets. The sparse matrix model (SML), developed at MIT, is a generalization of the traditional linear method that is capable of handling even large data sets. In this paper, we analyze a large data set, the NIST P13-18 data set, to determine the performance of SML compared to the linear program. We find that SML outperforms the linear program in both number of samples taken and in the median accuracy. Furthermore, due to a better model fit, SML learns the same model parameters as the nonlinear program. This paper demonstrates that SML is a viable alternative to the nonlinear approach when analyzing high-dimensional data. References: [NEXT] Table 1. A1. Maximum-Likelihood Estimate of the Classifier Performance Fitting SML to the NIST P13-18 Data Set (n = 19,094) Source Model Error Log likelihood SML NIST P13-18 SML Linear program (r) 0.90 0.88 SML (r) + Linear Regression (r) + Regularized Linear Regression + Lasso 0.92 0.99 Table 2. Maximum-Likelihood Estimate of the Classifier Performance Fitting SML to the NIST P13-18 Data Set (n = 19,094) Source Model Error Log likelihood SML NIST P13-18 SML Linear program (r) 0.90 0.88 SML (r) + Linear Regression (r) + Regularized Linear Regression + Lasso 0.92 0.99 Table 3. Number of Data Points/Tests for SML and Linear Programs vs.

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Using UML to specify refers to the practice of utilizing the Unified Modeling Language (UML) to create a visual representation and documentation of a system, software, or business process.

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