| Advanced classification techniques for real-time signals in resource-constrained systems. | |
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MedLine Citation:
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PMID: 19063255 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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Automated classification of situational awareness data collected by autonomous vehicles is currently an unmet need in many applications. Classification algorithms developed at JHUAPL extend large margin classification (LMC) machine learning techniques to solve domain-specific problems such as those found in unmanned undersea vehicles systems. Common classification issues for the described systems include the following: (1) Asymmetric binary class membership, that is, a small amount of a target signal must be distinguished in a very large collection of data; (2) no silver-bullet features, i.e., robust classification requires many weak features, are used to distinguish targets from other signals; and (3) limited processing resources. The JHUAPL solution uses existing LMC technology with modifications to solve specific domain issues: (1) addition of a penalty term to address class asymmetry, (2) an iterative training algorithm that yields a sparse solution optimized for a given computational footprint, and (3) featureless classification that requires minimal or no data reduction and can improve classification robustness of the algorithm and cut development costs. Use of these developed techniques is demonstrated using well studied open source data that are representative of that required for autonomous vehicle classification tasks. |
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Authors:
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G Scott Peacock; David Barsic; Ashley Llorens |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: The Journal of the Acoustical Society of America Volume: 124 ISSN: 1520-8524 ISO Abbreviation: J. Acoust. Soc. Am. Publication Date: 2008 Oct |
Date Detail:
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Created Date: 2008-12-09 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 7503051 Medline TA: J Acoust Soc Am Country: United States |
Other Details:
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Languages: eng Pagination: 2520 Citation Subset: IM |
Affiliation:
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Johns Hopkins Appl. Phys. Lab, 11100 Johns Hopkins Rd., Laurel, MD 20723-6099. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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