CAREER: Discriminative and Generative Machine Learning with Applications in Tracking and Gesture Recogniton

职业：判别式和生成式机器学习及其在跟踪和手势识别中的应用

• 批准号: 0347499
• 负责人: Tony Jebara
• 金额: --
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-15 至 2010-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0347499&HistoricalAwards=false
• 关键词: CAREER; Discriminative; Generative; Machine; Learning

This project aims to develop a tighter integration of generative (e.g., Bayesian networks) and discriminative (e.g., support vector machines) machine learning. These two types of learning are typically not integrated in current practice and are often seen as competing approaches; however, such integration is crucial in complex multi-disciplinary domains, such as vision, speech, and computational biology, where scientists have real expertise and exploitable knowledge about elaborate systems yet also need machine learning to achieve optimal performance for specific tasks. This project's integrated generative-discriminative framework will allow practitioners to flexibly design and structure a given learning problem using generative tools like Bayesian networks and then to maximize the performance of these models using discriminative methods like maximum entropy and probabilistic kernels. This framework will be used in computer vision tracking applications and in classification of gestures. In a laparoscopic robotic surgery platform, the methods will be used for classifying surgical drill movements and predicting surgeon dexterity level. The project's unified approach will be used to create a more comprehensive machine learning course experience for students, complete with online class materials, visual demonstrations and software toolkits.

该项目旨在开发一个更紧密的集成生成（例如，贝叶斯网络）和判别（例如，支持向量机）机器学习。这两种类型的学习在当前的实践中通常没有整合，并且通常被视为相互竞争的方法;然而，这种整合在复杂的多学科领域中至关重要，例如视觉，语音和计算生物学，科学家拥有关于复杂系统的真实的专业知识和可利用的知识，但也需要机器学习来实现特定任务的最佳性能。该项目的综合生成判别框架将允许从业者使用贝叶斯网络等生成工具灵活地设计和构建给定的学习问题，然后使用最大熵和概率内核等判别方法最大化这些模型的性能。该框架将用于计算机视觉跟踪应用和手势分类。在腹腔镜机器人手术平台中，该方法将用于对手术钻运动进行分类并预测外科医生的灵活性水平。该项目的统一方法将用于为学生创建更全面的机器学习课程体验，包括在线课程材料，视觉演示和软件工具包。