Machine Learning for Signal Analysis and System Modeling: Sparse and Event Driven Strategies

用于信号分析和系统建模的机器学习：稀疏和事件驱动策略

• 批准号: RGPIN-2017-05939
• 负责人: Pawlak, Miroslaw
• 金额: $ 1.89万
• 依托单位: University of Manitoba
• 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=635105
• 关键词: Machine; Learning; Signal; Analysis; System

The proposed research is in the area of machine learning (ML) and its applications to signal processing and system modelling. ML is one the fastest growing fields of computer science, with far-reaching applications. These diverse applications demand for new ML algorithms able to cope with massive data sets of high dimensionality. In this proposal, I describe my plan to extend my current research on nonparametric/semiparametric learning methods to ML problems that are driven by two concepts: sparsity and events.

拟议的研究是在机器学习（ML）及其应用领域的信号处理和系统建模。ML是计算机科学发展最快的领域之一，具有深远的应用。这些不同的应用需要新的ML算法，能够科普大量的高维数据集。在这个提案中，我描述了我的计划，将我目前对非参数/半参数学习方法的研究扩展到由两个概念驱动的ML问题：稀疏性和事件。