首页|Reports from Korea National University of Transportation Advance Knowledge in Machine Learning (Fast and Energy-Efficient Oblique Decision Tree Implementation with Potential Error Detection)
Reports from Korea National University of Transportation Advance Knowledge in Machine Learning (Fast and Energy-Efficient Oblique Decision Tree Implementation with Potential Error Detection)
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2024 FEB 02 (NewsRx) – By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news reporting originating from Korea National University of Transportation by NewsRx correspondents, research stated, “In the contemporary landscape, with the proliferation of cyber-physical systems and the Internet of Things, intelligent embedded systems have become ubiquitous.” Funders for this research include Ministry of Education. Our news editors obtained a quote from the research from Korea National University of Transportation: “These systems derive their intelligence from machine learning algorithms that are integrated within them. Among many machine learning algorithms, decision trees are often favored for implementation in such systems due to their simplicity and commendable classification performance. In this regard, we have proposed the efficient implementations of a fixed-point decision tree tailored for embedded systems. The proposed approach begins by identifying an input vector that might be classified differently by a fixed-point decision tree than by a floating-point decision tree. Upon identification, an error flag is activated, signaling a potential misclassification. This flag serves to bypass or disable the subsequent classification procedures for the identified input vector, thereby conserving energy and reducing classification latency."
Korea National University of TransportationCyborgsEmbedded SystemsEmerging TechnologiesMachine Learning
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