Knowledge Transfer and Reuse in Multiclassifier Systems

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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.***