Large scare semi-supervised pattern recognition and data mining from images and text

图像和文本的大规模半监督模式识别和数据挖掘

• 批准号: 372403-2009
• 负责人: Pal, Christopher
• 金额: $ 2.19万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2009
• 资助国家: 加拿大
• 起止时间: 2009-01-01 至 2010-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=435200
• 关键词: Large; scare; semi; supervised; pattern

Large scale pattern recognition and data mining techniques have resulted in broad and tangible impacts on applications ranging from improved general purpose internet search and semi-automated internet portal construction to accelerated discovery in bioinformatics. Many problems in pattern recognition and data mining arise in a setting where it is easy to obtain a small amount of labeled training data relative to the amount of available unlabeled data. Semi-supervised methods for pattern recognition allow labeled data to be combined with unlabeled data to improve the results of a recognition system. Information extraction is a type of data mining that seeks to extract structured database records from unstructured data. Once information is extracted it is possible to use the underlying structured representation for a variety of tasks such as enhancing search or subsequent data analysis. The research proposed here will develop new general purpose semi-supervised information extraction techniques. There is also a growing interest in addressing problems in pattern recognition and data mining can benefit from a tighter coupling of computer vision and text processing techniques. Concrete examples include: internet scale image search, object recognition with thousands of potential categories and the extraction of information from both images and text descriptions of biological experiments; however, many other emerging problems have similar properties. The research in this proposal thus also aims to develop principled and efficient methods for semi-supervised pattern recognition, data mining and information extraction with an emphasis on problems that involve the simultaneous processing of images and text. While the emphasis of this work will be on general purpose algorithms and techniques, to develop and evaluate techniques we propose to use the concrete scenarios of creating an extremely large scale visual encyclopedia by mining the web and creating algorithms also tailored to the more specialized settings of biological image and text analysis. Research will result in training highly qualified personnel in the development of techniques appropriate for internet scale and genome scale processing - skills in high demand.

大规模的模式识别和数据挖掘技术已经对各种应用产生了广泛和切实的影响，从改进的通用互联网搜索和半自动互联网门户建设到生物信息学中的加速发现。模式识别和数据挖掘中的许多问题出现在相对于可用的未标记数据量容易获得少量已标记训练数据的情况下。用于模式识别的半监督方法允许将已标记数据与未标记数据组合以改进识别系统的结果。信息抽取是一种数据挖掘，它试图从非结构化数据中提取结构化数据库记录。一旦提取了信息，就可以将基本的结构化表示法用于各种任务，例如加强搜索或后续数据分析。本文提出的研究将开发新的通用半监督信息抽取技术。人们对解决模式识别中的问题也越来越感兴趣，而数据挖掘可以受益于计算机视觉和文本处理技术的更紧密结合。具体的例子包括：互联网规模的图像搜索、具有数千个潜在类别的对象识别以及从生物实验的图像和文本描述中提取信息；然而，许多其他新出现的问题具有类似的特性。因此，这项建议的研究还旨在为半监督模式识别、数据挖掘和信息提取开发原则性和高效的方法，重点是涉及图像和文本同时处理的问题。虽然这项工作的重点将是通用算法和技术，但为了开发和评估技术，我们建议使用通过挖掘Web并创建算法来创建极大规模的可视百科全书的具体场景，这些算法也是为生物图像和文本分析的更专门设置而定制的。研究将导致培训高素质的人员，开发适合互联网规模和基因组规模处理的技术--这些技能需求很高。