Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
The root mean Square Deviation (RMSD) is the most common metric for measuring structural similarity between two structures. It is typically used in molecular biology, chemistry, and computational ...
Abstract: An open-circuit voltage (OCV) model, which represents OCV as a function of state of charge (SOC), is essential for estimating the state of a battery. Typically, the OCV-SOC characteristic is ...
Abstract: We consider the problem of online sparse linear approximation, where a learner sequentially predicts the best sparse linear approximations of an as yet unobserved sequence of measurements in ...
Linear multiplicative models are popular tools for analyzing data with positive responses. However, the linear structure of models is too restrictive on the regression relation, which may lead to a ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The partial Hessian approximation is often used in vibrational analysis of quantum ...
Transportation Science, Vol. 38, No. 3 (August 2004), pp. 343-356 (14 pages) Bounds and approximate formulae are developed for the average optimum distance of the transportation linear programming ...
Article subjects are automatically applied from the ACS Subject Taxonomy and describe the scientific concepts and themes of the article. One of the first studies on predicting redox potentials with ...
This study proposes a hybrid method to control dynamic time-varying plants that comprises a neural network controller and a cerebellar model articulation controller (CMAC). The neural-network ...
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