EFFICIENT MACHINE LEARNING METHODS FOR BIG DATA ANALYTICS

用于大数据分析的高效机器学习方法

• 批准号: RGPIN-2016-04855
• 负责人: LameirasKoerich, Alessandro
• 金额: $ 1.6万
• 依托单位: École de technologie supérieure
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634675
• 关键词: EFFICIENT; MACHINE; LEARNING; METHODS; BIG

Currently, big data plays a key role in science and in industry. Big data concern large volume of complex and growing datasets coming from multiple sources. There is a growing demand for approaches that are able to explore large volumes of data and extract useful information or knowledge for future actions. This large amount of data currently available provides an unprecedented opportunity to extract information and to answer several questions that were previously considered intangible. However, several challenges must be met to unveil the great potential of big data analytics.

目前，大数据在科学和工业中发挥着关键作用。大数据涉及来自多个来源的大量复杂且不断增长的数据集。对能够探索大量数据并为未来行动提取有用信息或知识的方法的需求日益增长。目前大量可用的数据提供了一个前所未有的机会来提取信息，并回答一些以前被认为是无形的问题。然而，要揭示大数据分析的巨大潜力，必须应对一些挑战。