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; 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，以解决结构监督的学习问题。现有的理论和算法的重点是从训练示例中学习未知功能，其中未知功能从特征向量到少数类之一。科学和工业中的新兴应用需要学习更复杂的功能，这些功能从复杂的输入空间（例如二维图，时间序列，序列和图形）映射到复杂的输出空间（例如其他二维图，时间序列，序列，序列和图）。示例包括在交易序列中检测欺诈交易，将语音的一部分（名词，动词等）分配给句子中的每个单词，在DNA序列中识别基因，并将土地覆盖类别分配给遥感图像中的每个像素。但是，现有的统计，机器学习和数据挖掘程序包并未为这些复杂的任务提供任何支持，也没有开发机器学习理论来分析这些任务。该项目将开发出结构监督学习（SSL）问题的一般表述，设计和测试了解决SSL问题的算法的集合，并开发了一种原型系统，该原型系统将为从业人员提供“ OFF -THE SHELF”工具，以便从业者开发SSL应用程序。该项目将产生更广泛的影响。首先，与社会面临的许多重要问题涉及在顺序，空间或结构数据中找到模式。这些包括（a）执法挑战，例如检测信用卡和健康保险欺诈的盗窃，（b）安全挑战，例如检测恐怖分子在运输集装箱中派弹炸弹的尝试，（c）健康和安全应用程序，例如检测疾病暴发的疾病爆发，从时间和空间造成的急诊室内疾病，例如急诊室的应用程序，例如（DID），例如，急诊室的应用程序（DID）疾病（DID）疾病（DID）疾病（DID）疾病（DID）疾病（DID）疾病（D）遥感图像。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（女性人数不足，在计算机科学方面），并为她们提供了参加科学会议和讲习班的机会。