Incorporating Prior Domain Knowledge into a Support Vector Machine Classifier with Explanation-Based Learning

通过基于解释的学习将先验领域知识纳入支持向量机分类器

• 批准号: 0413161
• 负责人: Gerald DeJong
• 金额: --
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413161&HistoricalAwards=false
• 关键词: Incorporating; Prior; Domain; Knowledge; into

This project addresses a central problem in machine learning: Can background knowledge, even though approximate and imperfect, improve learning accuracy and efficiency? This project combines inductive learning using Support Vector Machines (SVMs) with a new variant of explanation-based learning (EBL). Recognizing handwritten Chinese characters is the test domain. A limited training set is augmented with background knowledge of pen strokes: how they generate characters and how they can result in image pixels. EBL and SVMs are combined in two ways: (1) "phantom examples" are generated to enable learning with fewer input examples, and (2) EBL is used to generate new kernel functions for the SVM. The expected scientific advances include machine recognition of pictogram characters, concept learning in domains where training examples are scarce, and explanation-based learning with imperfect domain theories. Potential broader impacts include more automated preservation and dissemination of historical Chinese texts through the production of clean machine-readable copies and easier application of conventional machine translation systems to pictogram languages without the stumbling block of image input; automatic processing of pictogram and other line-drawing input may in turn enable new computer-based educational applications and opportunities. Integrating prior knowledge and inductive machine learning may lead to more cognitively plausible algorithms utilizing training experiences of a more human scale.

该项目解决了机器学习中的一个核心问题：即使近似和不完美，背景知识也可以提高学习准确性和效率吗？该项目使用支持向量机（SVM）将归纳学习与基于解释的学习（EBL）的新变体相结合。识别手写的汉字是测试域。有限的训练集通过笔触的背景知识来增强：它们如何产生角色以及如何产生图像像素。 EBL和SVM以两种方式组合在一起：（1）生成“幻影示例”以使学习示例更少，并且（2）EBL用于为SVM生成新的内核函数。预期的科学进步包括机器识别象形文字字符，在培训示例稀缺的领域中学习的概念学习以及具有不完美域理论的基于解释的学习。潜在的更广泛的影响包括通过生产干净的机器可读副本以及将传统的机器翻译系统更轻松地应用于象形图语言，而没有图像输入的绊脚石来更容易地应用传统的机器翻译系统；象形图和其他线绘制输入的自动处理可能反过来促进新的基于计算机的教育应用程序和机会。整合先前的知识和归纳机器学习可能会导致使用更人性化的培训经验，从而提高认知上的合理算法。