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Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition

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Title: Multiple Instance Learning with Applications to Concept Learning, Classification and Structural Pattern Recognition
Author: Xu, Tao
Department: School of Computer Science
Program: Computer Science
Advisor: Chiu, David
Abstract: In multiple instance learning (MIL), a class label is assigned to a collection (called bag) of instances instead of the individual instances. To learn concepts from a set of collectively labeled bags, MIL assumes that a bag is positive if and only if at least one instance in the bag is relevant to the target concept or negative otherwise. This research focuses on MIL in generating concept estimates (probably composed of multiple sub-concepts) that are not only expressive in terms of describing the class characteristics, but also useful in classifying new samples. For applications where sub-concepts are statistically independent and structurally unrelated, we propose two criteria: 1) adaptive kernel diverse density estimate (AKDDE) method that considers sub-concepts as the “peaks” of dense or high probabilistic regions of diverse positive samples; and 2) maximum partial entropy (MPE) that compares both the within-class similarity and the between-class dissimilarity. Taking into account both the statistical dependencies and the structural relations, we propose a common random subgraph (CRSG) model that represents a commonly observed subgraph pattern with probabilistically varying attributes of a class. The experimental results on both artificial and benchmark datasets demonstrate the adequacy and the advantage of the proposed methods, in describing the conceptual components, and at the same time effective in classifying new samples.
URI: http://hdl.handle.net/10214/10405
Date: 2017-04
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