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Model extrapolations, disturbances or sensor faults are first detected through a combined statistic (that considers the calibration region); then, a diagnosis is made by combining statistics pattern recognition, contribution analysis, and disturbance isolation based on historical fault patterns.
This area brings together the fields of statistics, pattern recognition, and machine learning to extract knowledge and detect patterns from complex sets of data (Castro et al. 2007).
Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures.
In the past few years, sparse representation (SR) has proven to be a powerful tool for computer vision, computational biology, statistics, pattern recognition, and other applications [32, 52, 53].
Quadratic discriminant analysis (QDA) is a widely used machine learning method among statistics, pattern recognition, and signal processing to find a quadratic combination of features which are responsible for characterizing an example into two or more categories.
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Significant advances have occurred in sensors, geodesy, photogrammetry, geophysics, computer science, statistics, and pattern recognition.
Clearly, for all five statistics, the pattern remains highly consistent across individuals and their combination.
The Artificial Neural Network (ANN) method uses computer algorithms (machine learning), applying non-linear statistics to pattern recognition.
All multivariate statistics and pattern recognition were performed using the Eigen victor toolbox (ver6.2.1) with two techniques: PCA and PLS on the Matlab.
ROI summary statistics, including pattern similarity estimates, were extracted with in-house scripts (Source code 1) in MATLAB 2009b (The MathWorks, Inc., Natick, MA), and statistical comparisons were conducted in R version 3.1.1 (http://www.R-project.org).org
The sufficient statistics of pattern counts for this data with total number of sites v = 895 under the CFN model over the space of all three-leaved phylogenetic trees are: (c xxx, c xxy, c yxx, c xyx) = (762, 54, 41, 38) Let human, chimpanzee and gorilla be denoted by leaf labels 1, 2 and 3, or H, C and G, respectively.
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