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The identification of context-sensitive features: A formal definition of context for concept learning

Turney, Peter (1996) The identification of context-sensitive features: A formal definition of context for concept learning. [Conference Paper]

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Abstract

A large body of research in machine learning is concerned with supervised learning from examples. The examples are typically represented as vectors in a multi- dimensional feature space (also known as attribute-value descriptions). A teacher partitions a set of training examples into a finite number of classes. The task of the learning algorithm is to induce a concept from the training examples. In this paper, we formally distinguish three types of features: primary, contextual, and irrelevant features. We also formally define what it means for one feature to be context-sensitive to another feature. Context-sensitive features complicate the task of the learner and potentially impair the learner's performance. Our formal definitions make it possible for a learner to automatically identify context-sensitive features. After context-sensitive features have been identified, there are several strategies that the learner can employ for managing the features; however, a discussion of these strategies is outside of the scope of this paper. The formal definitions presented here correct a flaw in previously proposed definitions. We discuss the relationship between our work and a formal definition of relevance.

Item Type:Conference Paper
Subjects:Computer Science > Artificial Intelligence
Computer Science > Machine Learning
Computer Science > Statistical Models
ID Code:1866
Deposited By: Turney, Peter
Deposited On:11 Nov 2001
Last Modified:11 Mar 2011 08:54

References in Article

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