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，机器学习的影响力受到了底层归纳框架的阻碍，该框架在很大程度上还没有从仅使用标记的概念实例（例如，电子邮件和关于它们是否是垃圾邮件的是/否标签）及其对用户角色的过于简单的看法或主题专家（SME）仅仅是训练实例标签的提供者。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好的正面例子？属于不同类的实例之间的主要区别是什么？哪些属性是瞬时的，哪些是不变的？ 学习者应该将注意力集中在哪里？ 当前的学习任务与先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，但它们也可以有效地减少假设空间，并在学习中提供比仅使用类成员反馈所能实现的显著速度。该项目的目的是通过开发框架以及可以利用更充分的混合-主动沟通。特别是，该项目旨在开发可以利用SME信息的ML算法，例如（1）地标实例的识别;（2）提出经验法则;（3）提供实例相似性的反馈;以及（4）相似性度量本身的转移。该项目带来了四个研究流：（1）基于相似性函数和地标实例的算法;（2）主动和“主动”学习;（3）贝叶斯主动迁移学习;（4）学习科普底层数据分布的时间演变。为了达到实际效果，该项目侧重于最需要这些新方法并且可能证明最有效的挑战，例如在具有概念漂移的动态环境中学习，以及存在长期迁移学习潜力的地方。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，实现更有效的学习。 教育和研究-社区外联包括霍华德大学的毕业生和本科生参加，例如参加项目所有学生参加的年度研究聚会，以及可重复使用的开放源码方法和数据集。

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.