RI: Small: Ensemble Methods for Structured Prediction

RI：小型：结构化预测的集成方法

• 批准号: 1117591
• 负责人: Mehryar Mohri
• 金额: $ 40.71万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1117591&HistoricalAwards=false
• 关键词: RI; Small; Ensemble; Methods; Structured

Ensemble methods are general techniques in machine learning for combining several hypotheses to create a more accurate predictor. In the batch learning setting, techniques such as bagging, boosting, stacking, error-correction techniques, Bayesian averaging, or other averaging schemes are common instances of these methods. These methods often significantly improve performance in practice and often benefit from favorable learning guarantees, typically in terms of the margins of the training samples. However, ensemble methods and their theory have been developed primarily for the common binary classification problem, or standard regression tasks where the target labels are real numbers and thus have no structure. These techniques do not readily apply to structured prediction problems such as pronunciation modeling, speech recognition, parsing, machine translation, or image processing. The objective of this proposal is to create the theoretical foundation, large-scale algorithms, and practical techniques for devising effective ensembles of structured prediction techniques. The benefits of these algorithms are likely to be at least as significant as those resulting from ensemble techniques in binary classification.Our solutions will be crucial to a broad set of applications and will be made widely accessible through open-source software programs. These software and open-source programs will make the use of our learning algorithms accessible to a broad community of researchers and engineers. More broadly, our techniques will benefit the society through the discovery of significantly more accurate solutions to a variety of important problems including speech recognition, speech synthesis, and machine translation.

集成方法是机器学习中的通用技术，用于组合多个假设以创建更准确的预测器。 在批量学习设置中，诸如装袋、提升、堆叠、纠错技术、贝叶斯平均或其他平均方案之类的技术是这些方法的常见实例。 这些方法在实践中通常显著提高性能，并且通常受益于良好的学习保证，通常是在训练样本的裕度方面。然而，集成方法及其理论主要是针对常见的二进制分类问题或标准回归任务开发的，其中目标标签是真实的数字，因此没有结构。这些技术不容易应用于结构化预测问题，如发音建模，语音识别，解析，机器翻译或图像处理。这个建议的目的是创建理论基础，大规模的算法，并设计有效的结构化预测技术的合奏实用技术。这些算法的好处可能至少是显着的集成技术在二进制classification.Our解决方案将是至关重要的广泛的应用程序集，并将通过开源软件程序被广泛访问。这些软件和开源程序将使广大的研究人员和工程师能够使用我们的学习算法。 更广泛地说，我们的技术将通过发现各种重要问题（包括语音识别，语音合成和机器翻译）的更准确的解决方案来造福社会。