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