Off-the-shelf Learning Algorithms for Structural Supervised Learning

用于结构监督学习的现成学习算法

• 批准号: 0307592
• 负责人: Thomas Dietterich
• 金额: --
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-06-01 至 2007-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0307592&HistoricalAwards=false
• 关键词: Off; shelf; Learning; Algorithms; Structural

This project will develop machine learning algorithms, software tools, and supporting theory for solving structural supervised learning problems. Existing theory and algorithms have focused on learning an unknown function from training examples, where the unknown function maps from a feature vector to one of a small number of classes. Emerging applications in science and industry require learning much more complex functions that map from complex input spaces (e.g., 2-dimensional maps, time series, sequences, and graphs) to complex output spaces (e.g., other 2-dimensional maps, time series, sequences, and graphs). Examples include detecting fraudulent transactions in a transaction sequence, assigning parts of speech (noun, verb, etc.) to each word in a sentence, identifying genes in DNA sequences, and assigning a land cover class to each pixel in a remote-sensed image. However, existing statistical, machine learning, and data mining packages do not provide any support for these complex tasks, nor has machine learning theory been developed to analyze these tasks. This project will develop a general formulation of the structural supervised learning (SSL) problem, design and test a collection of algorithms for solving SSL problems, and develop a prototype system that will provide "off -the-shelf " tools for practitioners to develop SSL applications. This project will have several broader impacts. First, many important problems confronting society involve finding patterns in sequential, spatial, or structural data. These include (a) law enforcement challenges, such as detecting theft of credit cards and health insurance fraud, (b) security challenges, such as detecting attempts by terrorists to send bombs in shipping containers, (c) health and safety applications, such as detecting outbreaks of diseases from temporal and spatial data about emergency room admissions, and (d) ecological applications, such as monitoring the health of ecosystems by analyzing sequences of remote-sensed images. The tools and techniques developed in this work can address all of these problems. Second, the project will train graduate students, including women (who are underrepresented in computer science), and provide them opportunities to attend scientific conferences and workshops.

该项目将开发机器学习算法、软件工具和支持理论来解决结构性监督学习问题。现有的理论和算法侧重于从训练示例中学习未知函数，其中未知函数从特征向量映射到少数类别之一。科学和工业中的新兴应用需要学习更复杂的函数，这些函数从复杂的输入空间（例如，二维地图、时间序列、序列和图形）映射到复杂的输出空间（例如，其他二维地图、时间序列、序列和图表）。示例包括检测交易序列中的欺诈交易、为句子中的每个单词分配词性（名词、动词等）、识别 DNA 序列中的基因以及为遥感图像中的每个像素分配土地覆盖类别。然而，现有的统计、机器学习和数据挖掘软件包没有为这些复杂任务提供任何支持，也没有发展出机器学习理论来分析这些任务。该项目将开发结构监督学习（SSL）问题的一般表述，设计和测试解决 SSL 问题的算法集合，并开发一个原型系统，为从业者开发 SSL 提供“现成”工具应用程序。该项目将产生几个更广泛的影响。首先，社会面临的许多重要问题都涉及在顺序、空间或结构数据中寻找模式。其中包括 (a) 执法挑战，例如检测信用卡盗窃和健康保险欺诈，(b) 安全挑战，例如检测恐怖分子在集装箱中发送炸弹的企图，(c) 健康和安全应用，例如从急诊室入院的时间和空间数据中检测疾病的爆发，以及(d)生态应用，例如通过分析遥感图像序列来监测生态系统的健康状况。这项工作中开发的工具和技术可以解决所有这些问题。其次，该项目将培训研究生，包括女性（计算机科学领域代表性不足），并为她们提供参加科学会议和研讨会的机会。