RI: Small: Collaborative Research: On-Line Learning Algorithms for Path Experts with Non-Additive Losses

RI：小型：协作研究：具有非加性损失的路径专家的在线学习算法

• 批准号: 1619271
• 负责人: Manfred Warmuth
• 金额: $ 17.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1619271&HistoricalAwards=false
• 关键词: RI; Small; Collaborative; Research; Line

On-line learning algorithms are increasingly adopted as the key solution to modern learning applications with very large data sets of several hundred million or billion points. These algorithms process one sample at a time with an update per iteration that is typically computationally cheap and easy to implement. Additionally, these algorithms benefit from a rich theoretical foundation. The objective of this research is to advance on-line learning by broadening its applicability to a variety of applications including machine translation, speech recognition, other natural language processing applications, handwriting recognition, computer vision, bioinformatics, and many other areas which can benefit the society. Expert skills and student talent will be combined to create effective theoretical and algorithmic solutions and open-source software tools tested experimentally that can benefit a wide community.Most learning problems admit some structure. In such problems, experts can be viewed as paths in a directed graph with each edge corresponding to a sub-structure corresponding to a word, phoneme, character, or image patch. Current on-line algorithms with path experts are limited to additive losses and therefore are not applicable in many important applications where the loss is non-additive. We will create the theoretical foundation for designing efficient on-line algorithms for learning with path experts with non-additive losses. Such non-additive losses are the relevant loss functions in most important applications such as machine translation, speech recognition, pronunciation modeling, parsing, image processing and other areas. Carefully designed weighted automata and semiring tools will be used to devise on-line algorithms for path experts with non-additive losses. The theoretical analysis of the algorithms will be complemented by a thorough experimental evaluation in a variety of applications.

在线学习算法越来越多地用作现代学习应用程序的关键解决方案，其数据集为数亿或十亿点。 这些算法一次处理一个样本，每次迭代的更新通常在计算上便宜且易于实现。此外，这些算法受益于丰富的理论基础。 这项研究的目的是通过扩大对机器翻译，语音识别，其他自然语言处理应用程序，手写识别，计算机视觉，生物信息学和许多其他可以使该领域受益的领域的应用程序来推进在线学习社会。专家技能和学生人才将结合起来，以创建有效的理论和算法解决方案以及经过实验测试的开源软件工具，可以使广泛的社区受益。大多数学习问题承认了一些结构。在此类问题中，专家可以被视为有向图中的路径，每个边缘对应于对应于单词，音素，角色或图像补丁的子结构。 当前具有路径专家的在线算法仅限于加性损失，因此不适用于许多损失不添加的重要应用。我们将创建理论基础，用于设计有效的在线算法，以与具有非添加损失的路径专家学习。 这种非加性损失是大多数重要应用中的相关损失函数，例如机器翻译，语音识别，发音建模，解析，图像处理和其他领域。 精心设计的加权自动机和半少量工具将用于为具有非添加损失的路径专家设计在线算法。 算法的理论分析将通过在各种应用中进行彻底的实验评估来补充。