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: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503443
• 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从改进的通用互联网搜索和半自动互联网门户构建到加速生物信息学发现的应用程序产生了广泛而有形的影响。 在很容易获得少量标记的培训数据相对于可用的未标记数据的数量的情况下，在模式识别和数据挖掘方面存在许多问题。 半监督的模式识别方法允许将标记的数据与未标记的数据结合使用，以改善识别系统的结果。 信息提取是一种旨在从非结构化数据中提取结构化数据库记录的数据挖掘。一旦提取信息，就可以将基础结构化表示形式用于各种任务，例如增强搜索或随后的数据分析。 这里提出的研究将开发新的通用半监督信息提取技术。 也对解决模式识别问题的问题也越来越兴趣，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耦合。 具体示例包括：Internet比例图像搜索，具有数千个潜在类别的对象识别以及从图像和生物学实验的文本描述中提取信息；但是，许多其他新兴问题具有相似的属性。 因此，该提案中的研究还旨在开发针对半监督模式识别，数据挖掘和信息提取的原则有效方法，重点是涉及同时处理图像和文本的问题。 尽管这项工作的重点将放在通用算法和技术上，但要开发和评估技术，我们建议通过挖掘网络并创建算法量身定制的算法，也针对更专业的生物图像和文本分析来创建算法，以创建极大的视觉百科全书。 研究将导致培训高素质的人员，以开发适合互联网规模和基因组规模处理的技术 - 需求量高。