RI: Medium: Interactive Transfer Learning in Dynamic Environments

机器学习(ML)在建立坚实的理论基础和扩展到从科学(例如计算生物学)到实践(例如金融欺诈检测、垃圾邮件检测)的主要应用方面都取得了巨大的成功。然而，机器学习的范围一直受到底层归纳框架的阻碍，该框架在很大程度上没有从仅使用概念的标签实例(例如，电子邮件和关于它们是否为垃圾邮件的是/否标签)演变而来，以及其过于简单地将用户或主题专家(SME)的角色视为仅仅提供训练实例的标签。然而，在指导人类时，教师提供了更丰富的信息：为什么一个概念的实例是一个很好的积极例子？属于不同类的实例之间的主要区别是什么？哪些属性是瞬变的，哪些是不变的？学习者应该把注意力集中在哪里？当前的学习任务与以前获得的概念或过程有什么共同之处？对这些问题的回答不仅丰富了学习过程，而且与仅使用班级成员反馈相比，它们可以有效地缩小假设空间并显著提高学习速度。本项目的目的是通过开发能够利用更充分的混合主动交流的框架和机器学习方法，将这种更丰富的交互引入机器学习领域。特别是，该项目旨在开发可以利用来自中小企业的信息的ML算法，例如(1)地标实例的识别；(2)提出经验规则；(3)提供关于实例相似性的反馈；以及(4)相似性度量本身的转移。这个项目带来了四个方面的研究：(1)基于相似函数和标志性实例的算法；(2)主动和“主动”学习；(3)贝叶斯主动迁移学习；以及(4)学习如何应对底层数据分布中的时间演变。为了取得实际成果，本项目侧重于最需要并可能证明最有效的这些新方法的挑战，例如在概念漂移的动态环境中的学习，以及在存在长期迁移学习潜力的环境中的学习。更广泛的影响包括通过将科学领域的知识纳入电子科学，例如在计算蛋白质组学中进行更有效的学习。教育和研究--社区外展包括霍华德大学的毕业生和本科生参加，例如，参加每年一次的研究聚会，让项目中的所有学生参加，以及可重复使用的开源方法和数据集。

Machine learning (ML) has witnessed tremendous success both in establishing firm theoretical foundations and reaching out to major applications ranging from the scientific (e.g. computational biology) to the practical (e.g. financial fraud detection, spam detection). However the reach of machine learning has been hampered by an underlying inductive framework that largely has not evolved from using only labeled instances of concepts (e.g. emails and yes/no labels on whether they are spam) and its overly simple view of the role of the user or subject matter expert (SME) as a mere provider of the labels for the training instances. However, when instructing humans, teachers provide richer information: Why is an instance of a concept a good positive example? What are key differences between instances belonging to different classes? Which properties are transient and which are invariant? Where should the learner focus attention? What does the current learning task have in common with previously acquired concepts or processes? Answers to such questions not only enrich the learning process, but they also can effectively reduce the hypothesis space and provide significant speed ups in learning than can be achieved with use of class membership feedback only.The aim of this project is to bring this kind of richer interaction into the realm of machine learning by developing frameworks as well as machine learning methods that can take advantage of fuller mixed-initiative communication. In particular, this project aims to develop ML algorithms that can exploit information from SME's such as (1) identification of landmark instances; (2) proposing rules of thumb; (3) providing feedback on similarity of instances; and (4) transfer of similarity measures themselves. This project brings to bear four streams of research: (1) algorithms based on similarity functions and landmark instances; (2) active and "pro-active" learning; (3) Bayesian active transfer learning; and (4) learning to cope with temporal evolution in the underlying data distribution. In order to reach practical results, this project focuses on challenges where these new methods are both most needed and likely to prove most effective, such as learning in dynamic environments with concept drift, and where potential for long-term transfer learning is present. Broader impacts include more effective learning by incorporating scientific domain knowledge in eScience, for instance in computational proteomics. Educational and research-community outreach includes participation of graduates and undergraduates from Howard University, for instance in yearly research gatherings involving all students on the project, and reusable open-source methods and data sets.