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

该项目旨在开发生成性(例如，贝叶斯网络)和判别性(例如，支持向量机)机器学习的更紧密的集成。这两种类型的学习在当前的实践中通常没有整合在一起，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是至关重要的，例如视觉、言语和计算生物学，在这些领域，科学家拥有关于复杂系统的真正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。该项目的集成生成-判别框架将允许实践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上，这些方法将被用于对手术钻头的动作进行分类，并预测外科医生的灵巧度水平。该项目的统一方法将用于为学生创造更全面的机器学习课程体验，包括在线课堂材料、可视化演示和软件工具包。

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.