首页|University of Ottawa Researchers Further Understanding of Machine Learning (Mult i-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, a nd Applications)
University of Ottawa Researchers Further Understanding of Machine Learning (Mult i-Label Lifelong Machine Learning: A Scoping Review of Algorithms, Techniques, a nd Applications)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Research findings on artificial intelligence are discussed in a new report. According to news reporting out of Ottawa, Canada, by NewsRx editors, research stated, "Lifelong machine learning concerns the develo pment of systems that continuously learn from diverse tasks, incorporating new k nowledge without forgetting the knowledge they have previously acquired." Financial supporters for this research include Natural Sciences And Engineering Research Council of Canada. Our news editors obtained a quote from the research from University of Ottawa: " Multi-label classification is a supervised learning process in which each instan ce is assigned multiple non-exclusive labels, with each label denoted as a binar y value. One of the main challenges within the lifelong learning paradigm is the stability-plasticity dilemma, which entails balancing a model's adaptability in terms of incorporating new knowledge with its stability in terms of retaining p reviously acquired knowledge. When faced with multi-label data, the lifelong lea rning challenge becomes even more pronounced, as it becomes essential to preserv e relations between multiple labels across sequential tasks. This scoping review explores the intersection of lifelong learning and multi-label classification, an emerging domain that integrates continual adaptation with intricate multi-lab el datasets. By analyzing the existing literature, we establish connections, ide ntify gaps in the existing research, and propose new directions for research to improve the efficacy of multi-label lifelong learning algorithms. Our review une arths a growing number of algorithms and underscores the need for specialized ev aluation metrics and methodologies for the accurate assessment of their performa nce."
University of OttawaOttawaCanadaNo rth and Central AmericaAlgorithmsCyborgsEmerging TechnologiesMachine Lea rning
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