ITR: From Bits to Information: Statistical Learning Technologies for Digital Information Management and Search

现代统计学习方法有望在提供更强大的工具来从比特中获取信息方面发挥关键作用，这是互联网面临的一个至关重要且日益严重的问题。该项目的目标是通过开发和扩展新的统计学习理论和算法，开发一种管理、组织和搜索多媒体数字信息的新技术。在此过程中，我们期望对关键系统组件进行原型设计，并开发科学见解。该研究的预期结果是：(1)新的学习算法和相关表示，可用于对文本、图像和视频进行分类；(2)对这些学习算法和查询回答方法进行新的理论分析；(3)对分类和路由电子邮件消息以及在网络上搜索、分类和提取信息的原型系统进行演示和评估。更智能的多媒体数据分类软件是实现第二波更智能的互联网技术浪潮的先决条件。路由、组织和搜索信息的自动技术是帮助个人和组织利用计算机网络正在创造的数据海洋所必需的。这类项目的成功将使这一步成为可能，并加速互联网的发展。

Modern statistical learning approaches are expected to play a key role in providing more powerful tools to harvest information from bits, a crucial and growing problem for the Internet. The goal of this project is thus to develop a new technology for the management, organization, and search of multimedia digital information by exploiting and extending new statistical learning theories and algorithms. In the process we expect to prototype key system components and to develop scientific insights. Anticipated outcomes of the research are (1) new learning algorithms and associated representations that can be applied to categorize text, images, and video, (2) new theoretical analyses of these learning algorithms and query-answering methods and (3) demonstrations and evaluations of prototype systems for classifying and routing email messages and searching, categorizing, and extracting information on the Web.Smarter classification software for multimedia data is a prerequisite to enable a second, more intelligent wave of Internet technologies. Automatic techniques to route, organize and search information are needed to help individuals and organizations exploit the sea of data that the computer networks are creating. The success of projects like this will make such a step possible and accelerate the evolution of the Internet.