RI: Small: Active Learning with Rich Query Types on Networks and Trees

RI：小型：网络和树上具有丰富查询类型的主动学习

• 批准号: 1218880
• 负责人: Mark Craven
• 金额: $ 44.99万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1218880&HistoricalAwards=false
• 关键词: RI; Small; Active; Learning; Rich

Supervised machine learning is a critical component of software systems in a wide variety of applications. Although models induced via supervised learning algorithms often provide state-of-the-art accuracy, they are not applied as widely as they could be because they require labeled training instances, which are often expensive to acquire. One promising approach to addressing this limitation is to employ active learning algorithms. These methods are able to make queries in which they choose which instances are labeled and added to the training set. The goal of this project is to develop a new class of algorithms for active learning that broadens the applicability of this approach to more complex, realistic settings. Specifically, we will develop methods that (i) address complex learning tasks such as biological network reconstruction and event extraction from natural language, (ii) assemble batches of queries when it is cost effective to do so, (iii) are able to employ a variety of query types, and (iv) reason about the costs incurred for various queries.Machine learning represents an important methodology for inferring models that can make useful predictions in scientific, educational, health-care, business and consumer applications. The methods to be developed in this project will provide substantial benefits to machine-learning applications in such problem domains by reducing the cost required to obtain enough data to learn accurate models. Moreover, because this project is connected to specific collaborations with biologists, it is likely to have a direct impact on the ability of scientists to design, conduct and interpret experiments investigating networks of complex relationships such as host-virus interactions. The project will also play a role in training undergraduate and graduate students in interdisciplinary research, and in recruiting undergraduate students from under-represented minority groups into scientific careers.

监督机器学习是各种应用中软件系统的关键组成部分。 虽然通过监督学习算法诱导的模型通常提供最先进的准确性，但它们的应用并不广泛，因为它们需要标记的训练实例，而这些实例的获取通常很昂贵。 解决这一限制的一种有前途的方法是采用主动学习算法。 这些方法能够进行查询，其中它们选择标记哪些实例并将其添加到训练集中。 该项目的目标是开发一种新的主动学习算法，将这种方法的适用性扩展到更复杂，更现实的环境中。 具体来说，我们将开发方法，（i）解决复杂的学习任务，如生物网络重建和从自然语言中提取事件，（ii）组装批量查询时，它是成本效益这样做，（iii）能够采用各种查询类型，及（iv）机器学习代表了一种重要的方法，用于推断模型，这些模型可以在科学、教育、保健、商业和消费者应用。 在这个项目中开发的方法将通过降低获得足够数据以学习准确模型所需的成本，为这些问题领域的机器学习应用提供实质性的好处。此外，由于该项目与生物学家的具体合作有关，因此很可能对科学家设计、进行和解释研究宿主-病毒相互作用等复杂关系网络的实验的能力产生直接影响。 该项目还将在培训本科生和研究生进行跨学科研究方面发挥作用，并在招募代表性不足的少数群体的本科生从事科学事业方面发挥作用。