New machine learning methods for collaborative filtering, document retrieval and image retrieval

用于协同过滤、文档检索和图像检索的新机器学习方法

• 批准号: 356701-2007
• 负责人: Hinton, Geoffrey
• 金额: $ 7.07万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Strategic Projects Supplemental Competition
• 财政年份: 2007
• 资助国家: 加拿大
• 起止时间: 2007-01-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=365653
• 关键词: New; machine; learning; methods; collaborative

The research will use novel machine learning algorithms recently developed at the University of Toronto toimprove several aspects of web-based systems.Collaborative filtering systems recommend products such as books or DVD's to a user based on similaritiesbetween that user and other users whose preferences are known. There are many different ways of using thesesimilarities and new, non-linear machine learning methods are already more accurate than the methodscurrently in use. These methods will be developed further.A newly discovered method called "semantic hashing" is much faster than existing methods at retrievingdocuments that are similar to a query document. Semantic hashing uses machine learning to associate anaddress with each document in the database in such a way that similar documents have addresses that differ byonly a few bits. Given the address of the query document, similar documents can than be found very rapidly bysimply looking at neighboring addresses. The research will improve the ability of the machine learningalgorithms to assign similar addresses to similar documents. It will also apply similar methods to imageretrieval. This will involve learning features of images that are as informative about the content of an image aswords are about the content of a document.New methods will be developed for learning features from large databases of unlabeled satellite images, so thatclassifiers for types of ground cover can be learned with fewer labeled training examples. The classifiers willalso be improved by learning to use the contextual information provided by the labels of nearby regions.

这项研究将使用多伦多大学最近开发的新型机器学习算法来改进基于网络的系统的几个方面。协同过滤系统根据用户与其他偏好已知的用户之间的相似性向用户推荐书籍或DVD等产品。有许多不同的方法可以使用这些相似性，新的非线性机器学习方法已经比目前使用的方法更准确。这些方法将得到进一步的发展。一种新发现的称为“语义散列”的方法在检索与查询文档相似的文档时比现有方法快得多。语义散列使用机器学习将地址与数据库中的每个文档相关联，使得类似文档的地址仅相差几位。给定查询文档的地址，类似的文档可以通过简单地查看相邻的地址而非常快速地找到。该研究将提高机器学习算法为类似文档分配类似地址的能力。它也将应用类似的方法图像检索。这将涉及到学习图像的特征，这些特征对图像内容的信息量就像文字对文档内容的信息量一样。将开发新的方法来学习来自未标记卫星图像的大型数据库的特征，以便用更少的标记训练样本来学习地面覆盖类型的分类器。分类器也将通过学习使用由附近区域的标签提供的上下文信息来改进。