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1.6.2 (2 July 2022 ()) [3] Yes GNU GPL: CLI, GUI: C Perl (by PSPP-Perl [4]) R: R Foundation 4.4.1 (14 June 2024 ()) [5] Yes GNU GPL: CLI, GUI [6] C, Fortran, R [7] R language, Python (by RPy), Perl (by Statistics::R module) R++: Zebrys 1.6.15 (8 December 2023 ()) [8] No Proprietary: CLI, GUI: C++, Qt R language: RKWard: RKWard community
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...
Simon Haykin, Adaptive Filter Theory, Prentice Hall, 2002, ISBN 0-13-048434-2 M.H.A Davis, R.B. Vinter, Stochastic Modelling and Control , Springer, 1985, ISBN 0-412-16200-8 Weifeng Liu, Jose Principe and Simon Haykin, Kernel Adaptive Filtering: A Comprehensive Introduction , John Wiley, 2010, ISBN 0-470-44753-2
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Moving least squares is a method of reconstructing continuous functions from a set of unorganized point samples via the calculation of a weighted least squares measure biased towards the region around the point at which the reconstructed value is requested.
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. [1] Choosing informative, discriminating, and independent features is crucial to produce effective algorithms for pattern recognition, classification, and regression tasks.
GOOWE-ML [23]-based methods: Interpreting the relevance scores of each component of the ensemble as vectors in the label space and solving a least squares problem at the end of each batch, Geometrically-Optimum Online-Weighted Ensemble for Multi-label Classification (GOOWE-ML) is proposed. The ensemble tries to minimize the distance between the ...