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19 lines
990 B
Plaintext
19 lines
990 B
Plaintext
SVMlight is an implementation of Vapnik's Support Vector Machine
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[Vapnik, 1995] for the problem of pattern recognition, for the problem
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of regression, and for the problem of learning a ranking function. The
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optimization algorithms used in SVMlight are described in [Joachims,
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2002a ]. [Joachims, 1999a]. The algorithm has scalable memory
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requirements and can handle problems with many thousands of support
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vectors efficiently.
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The software also provides methods for assessing the generalization
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performance efficiently. It includes two efficient estimation methods
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for both error rate and precision/recall. XiAlpha-estimates [Joachims,
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2002a, Joachims, 2000b] can be computed at essentially no
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computational expense, but they are conservatively biased. Almost
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unbiased estimates provides leave-one-out testing. SVMlight exploits
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that the results of most leave-one-outs (often more than 99%) are
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predetermined and need not be computed [Joachims, 2002a].
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WWW: http://svmlight.joachims.org/
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