摘要
由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。据新闻报道By NewsRx记者从美国波士顿报道，研究称，“不断增加的样本”功能近红外光谱(fNIRS)的尺寸和通道密度要求精确和可扩展识别不允许进行可靠分析的信号。尽管在检测这些“坏通道”时，对于fNIRS检测方法的行为知之甚少，并且无监督和半监督机器学习的潜力仍未得到探索。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Machine Learning is th e subject of a report. According to newsreporting from Boston, United States, b y NewsRx journalists, research stated, “The increasing samplesizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise a nd scalableidentification of signals that do not permit reliable analysis to ex clude them. Despite the relevance ofdetecting these ‘bad channels,’ little is k nown about the behavior of fNIRS detection methods, and thepotential of unsuper vised and semi-supervised machine learning remains unexplored.”
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