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.

这个项目解决了机器学习中的一个核心问题：背景知识，即使是近似的和不完善的，能提高学习的准确性和效率吗？该项目将使用支持向量机（svm）的归纳学习与基于解释的学习（EBL）的新变体相结合。识别手写汉字是测试领域。有限的训练集增加了笔画的背景知识：它们如何生成字符以及它们如何产生图像像素。EBL和支持向量机以两种方式结合：(1)生成“幻影样例”以实现输入样例较少的学习；(2)使用EBL为支持向量机生成新的核函数。预期的科学进展包括象形文字的机器识别，训练样本稀缺领域的概念学习，以及不完善领域理论的基于解释的学习。潜在的更广泛影响包括，通过生产干净的机器可读副本，更自动地保存和传播历史中文文本，以及更容易地将传统的机器翻译系统应用于象形文字，而无需图像输入的障碍；象形文字和其他线条输入的自动处理反过来又可以实现新的基于计算机的教育应用和机会。整合先验知识和归纳机器学习可能会产生更多认知上合理的算法，利用更人性化的训练经验。