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This chapter discusses the synchronous machine model based on dqO coordinates.
An unbalanced steady-state machine model based on symmetrical components theory is developed as a mathematical tool to estimate different machine currents and total developed torque under open circuit phase(s).
Recently, Hazai et al. obtained a support vector machine model based on 5 Dragon descriptors [ 21].
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The report describes the development of a machine learning model based on an analysis of administrative data available for all 975,057 regular U.S. Army soldiers on active duty from 2004 to 2009.
In this study, we present a support-vector machine (SVM) model based on data from 1.6 million compounds in ChEMBL database with logD annotations from the ACD/logD algorithm.
Interactive learning focuses on enhancing an existing machine-learning model based on newly acquired information, which is not possible in a classical machine learning setting.
However, in general, the machine-learning model based on the Random Forest classifier showed the most robust performance.
Determining the answer to that question would require answering a host of others: How would a machine-learning model based on data collected in one region (e.g., New York) support predictions of people's political attitudes in another region (e.g., Texas)?
Machine learning –whether it is model based machine learning or deep learning – has the potential to train a model that will then be able to predict specific elements based on new input.
İç et al. (2012) developed a component-based machining centre selection model based on AHP, which would use only the technical specifications while evaluating the machining centre components.
An improved support vector machine (SVM) classification model based on a genetic algorithm (GA) is built in order to guarantee accurate estimation of different types of porosity defects.
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