摘要
一位新闻记者兼机器人与机器学习的工作人员新闻编辑每日新闻-在一份新的报告中讨论了机器学习的研究结果。根据NewsRx记者对中华人民共和国西安的新闻报道，研究表明，"现有的空间相关NOIS E下源数估计(SNE)的模型D riven方法受到模型假设和主观参数设置的固有缺陷的限制，"虽然机器学习(ML)以其强大的学习能力开始在SNE中出现，但现有的基于ml的方法主要针对高斯n白噪声，对空间相关噪声的研究较少。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Research findings on Machine Learning are discussed in a new report. According to news reporting from Xi’an, People’s Republic of China, by NewsRx journalists, research stated, “The existing model-d riven methods for source number estimation (SNE) under spatially correlated nois e are limited by the inherent shortcomings of model assumptions and subjective p arameter settings, and have high requirements for signal-to-noise ratio (SNR) an d sample size. Although machine learning (ML) has begun to emerge in SNE due to its powerful learning ability, existing ML-based methods mainly focus on Gaussia n white noise, and there are a few works on spatially correlated noise.”
NETL
NSTL