首页|Researchers from U.S. Geological Survey (USGS) Report on Findings in Machine Lea rning (A Probabilistic Approach To Training Machine Learning Models Using Noisy Data)
Researchers from U.S. Geological Survey (USGS) Report on Findings in Machine Lea rning (A Probabilistic Approach To Training Machine Learning Models Using Noisy Data)
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews – Current study results on Machine Learning have be en published. According to news originatingfrom Sacramento, California, by News Rx correspondents, research stated, “Machine learning (ML) modelsare increasing ly popular in environmental and hydrologic modeling, but they typically contain uncertaintiesresulting from noisy data (erroneous or outlier data). This paper presents a novel probabilistic approachthat combines ML and Markov Chain Monte Carlo simulation to (1) detect and underweight likely noisydata, (2) develop an approach capable of detecting noisy data during model deployment, and (3) interpret the reasons why a data point is deemed noisy to help heuristically distingu ish between outliers anderroneous data.”
SacramentoCaliforniaUnited StatesN orth and CentralAmericaAlgorithmsCyborgsEmerging TechnologiesMachine Le arningU.S. Geological Survey (USGS)
NETL
NSTL
