Knowledge Transfer and Reuse in Multiclassifier Systems

9900353ghosh对于涉及学习、适应、识别/分类和动态控制的难题，通常需要同时和协调地使用多个学习组件，以获得令人满意的鲁棒解决方案。在流行的多学习者方法中，如集成、贝叶斯模型平均器和专家混合，每个组件模型都试图解决相同任务（可能是局部版本）。然而，在实践中，人们经常面临一系列（可能相关的）任务，或者由于非平稳性，新的数据/传感器或新的领域知识，其性质随时间发生实质性变化的任务。此外，这些任务是在不同的时间点解决的，在任何给定的时间，我们都可以知道未来需要解决的任务。该项目将研究如何利用现有的“支持”模型来帮助解决新的和可能相关的任务。它将侧重于分类问题，尽管许多方法扩展到更广泛的任务。待审查的问题包括：(i)如何从若干支助模式中进行选择，并衡量其与新任务的相关性；（ii）仅使用支持模型的输入/输出行为的传递/重用机制，以及（iii）利用支持模型内部结构信息的机制。处理序列分类器时出现的某些特殊问题也将被研究。主要目标是利用封装在支持模型中的知识，以更少的新训练示例更快，更准确地学习，以及更好地理解新问题。该项目还将基于工程相关数据集，在拟议的领域建立一个基准设施，以造福研究界。这项工作将促进更多功能和强大的多组件方法在工程智能、自适应系统中的应用。它还将创建一个不断发展的支持模型知识库，这些支持模型是协作开发的，并且可以在未来的任务中重用

9900353GhoshSimultaneous and coordinated use of multiple learning components is often needed for satisfactory and robust solutions to difficult problems involving learning, adaptation, recognition/classification and dynamic control. In popular multi-learner approaches such as ensembles, Bayesian model averagers and mixtures of experts, each component model tries to solve (possibly localized versions of) the same task. However, in practice, one is often faced by a series of (possibly related) tasks, or a task whose nature changes substantially with time due to nonstationarities, new data/sensors or new domain knowledge. Moreover, these tasks are solved at different points in time, and at any given time, we may know about what future tasks will need to be solved.This project will study how existing "support" models can be leveraged to help solve new, and possible related tasks. It will focus on classification problems, though many of the approaches extend to a much wider variety of tasks. Issues to be examined include: (i) how to select from among several support models and measure their relevance vis a vis the new task; (ii) transfer/reuse mechanisms that only use input/output behavior of support models, and (iii) mechanisms that exploit internal structural information of support models. Certain special issues that arise when dealing with sequence classifiers will also be investigated. The main goal is to exploit knowledge encapsulated in support models for quicker, more accurate learning with fewer new training examples, along with better understanding of the new problem.This project will also establish a benchmarking facility in the proposed areas for the benefit of the research community, based on engineering related datasets. This work will facilitate the application of more versatile and powerful multi-component approaches to engineering smart, adaptive systems. It will also create an evolving knowledge base of support models that are developed collaboratively and are reusable for future tasks.***