Inductive transfer and machine life-long learning

归纳迁移和机器终身学习

• 批准号: 227458-2009
• 负责人: Silver, Daniel
• 金额: $ 1.75万
• 依托单位: Acadia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503392
• 关键词: Inductive; transfer; machine; life; long

Machine learning research has focused on inducing a model of a function or classification task strictly from a set of training examples. Consequently, machine learning systems do not take advantage of previously acquired task knowledge when learning a new and potentially related task. This is unfortunate since prior task knowledge offers the potential to produce more accurate models in less time and using fewer training examples. My research investigates machine life-long learning (ML3) systems (particularly, architectures of artificial neural networks) that are capable of retaining and selectively transferring task knowledge when learning a new task. Data mining and adaptive software systems can benefit from the use of inductive transfer and ML3 methods. For example, more accurate medical diagnostic models can be developed from small samples of patients by using previously learned and related diagnostic models. Adaptive systems can learn more efficiently the interaction model (profile) of a new user by selectively transferring knowledge from similar user models.

机器学习研究的重点是严格从一组训练示例中归纳出函数或分类任务的模型。 因此，机器学习系统在学习新的和潜在相关的任务时不利用先前获得的任务知识。 这是不幸的，因为先前的任务知识提供了在更短的时间内使用更少的训练示例生成更准确的模型的潜力。 我的研究调查了机器终身学习（ML3）系统（特别是人工神经网络的架构），这些系统能够在学习新任务时保留和选择性地转移任务知识。数据挖掘和自适应软件系统可以从使用感应传输和ML3方法中受益。 例如，通过使用先前学习的和相关的诊断模型，可以从患者的小样本开发更准确的医学诊断模型。 自适应系统可以通过选择性地从相似的用户模型中转移知识来更有效地学习新用户的交互模型（配置文件）。