27 lines
1.6 KiB
Markdown
27 lines
1.6 KiB
Markdown
# Explicit Loss Minimization
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QuaPy makes available several Explicit Loss Minimization (ELM) methods, including
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SVM(Q), SVM(KLD), SVM(NKLD), SVM(AE), or SVM(RAE).
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These methods require to first download the
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[svmperf](http://www.cs.cornell.edu/people/tj/svm_light/svm_perf.html)
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package, apply the patch
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[svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/svm-perf-quantification-ext.patch), and compile the sources.
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The script [prepare_svmperf.sh](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) does all the job. Simply run:
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```
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./prepare_svmperf.sh
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```
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The resulting directory `svm_perf_quantification/` contains the
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patched version of _svmperf_ with quantification-oriented losses.
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The [svm-perf-quantification-ext.patch](https://github.com/HLT-ISTI/QuaPy/blob/master/prepare_svmperf.sh) is an extension of the patch made available by
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[Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0)
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that allows SVMperf to optimize for
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the _Q_ measure as proposed by [Barranquero et al. 2015](https://www.sciencedirect.com/science/article/abs/pii/S003132031400291X)
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and for the _KLD_ and _NKLD_ measures as proposed by [Esuli et al. 2015](https://dl.acm.org/doi/abs/10.1145/2700406?casa_token=8D2fHsGCVn0AAAAA:ZfThYOvrzWxMGfZYlQW_y8Cagg-o_l6X_PcF09mdETQ4Tu7jK98mxFbGSXp9ZSO14JkUIYuDGFG0).
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This patch extends the above one by also allowing SVMperf to optimize for
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_AE_ and _RAE_.
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See the [](./methods) manual for more details and code examples.
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