Download | - View accepted manuscript: The Identification of Context-Sensitive Features: A Formal Definition of Context for Concept Learning (PDF, 512 KiB)
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Author | Search for: Turney, Peter1 |
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Affiliation | - National Research Council of Canada. NRC Institute for Information Technology
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Format | Text, Article |
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Conference | Proceedings of the Workshop on Learning in Context-Sensitive Domains,at the 13th International Conference on Machine Learning (ICML-96), July 3-6, 1996., Bari, Italy |
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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 afinite 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 ofthe 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. |
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Publication date | 1996 |
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Language | English |
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NRC number | NRCC 39222 |
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NPARC number | 8913933 |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | 9e8d83c0-f184-4786-b3fe-6d9613a0c01b |
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Record created | 2009-04-22 |
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Record modified | 2020-03-20 |
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