首页|New Machine Learning Findings Has Been Reported by Investigators at Arizona State University (Feasibility Study To Identify Machine Learning Predictors for a Virtual Environment Grocery Store)
New Machine Learning Findings Has Been Reported by Investigators at Arizona State University (Feasibility Study To Identify Machine Learning Predictors for a Virtual Environment Grocery Store)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating in Tempe, Arizona, by NewsRx journalists, research stated, "Virtual reality-based assessment and training platforms proffer the potential for higher-dimensional stimulus presentations (dynamic; three dimensional) than those found with many low-dimensional stimulus presentations (static; two-dimensional) found in pen-and-paper measures of cognition. Studies have investigated the psychometric validity and reliability of a virtual reality-based multiple errands task called the Virtual Environment Grocery Store (VEGS)." The news reporters obtained a quote from the research from Arizona State University, "While advances in virtual reality-based assessments provide potential for increasing evaluation of cognitive processes, less has been done to develop these simulations into adaptive virtual environments for improved cognitive assessment. Adaptive assessments offer the potential for dynamically adjusting the difficulty level of tasks specific to the user's knowledge or ability. Former iterations of the VEGS did not adapt to user performance. Therefore, this study aimed to develop performance classifiers from participants (N = 75) using three classification techniques: Support Vector Machines (SVM), Naive Bayes (NB), and k-Nearest Neighbors (kNN). Participants were categorized as either high performing or low performing based upon the number items they were able to successfully find and add to their grocery cart. The predictors utilized for the classification focused on the times to complete tasks in the virtual environment. Results revealed that the SVM (88% correct classification) classifier was the most robust classifier for identifying cognitive performance followed closely by kNN (86.7%); however, NB tended to perform poorly (76%)."
TempeArizonaUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningSupport Vector MachinesArizona State University