SLEECcode_多标签分类_源码

**The code requires prior installation of libinear The windows 64 bit binary of 'train' is already included in the sleec_train folder To run on other platforms, compile liblinear and use the matlab train executable for linux in sleec_train For parallelization, we have used openmp library, just make sure that the compiler you are using is OpenMP compatible** To compile SLEEC, run the following command on the Matlab prompt > make_SLEEC SLEEC training and prediction code have been combined with the SLEEC functions. To run SLEEC, try > load data.mat > SLEECparams > [result] = SLEEC(data, params); The following explains the matlab structure for the data, parameters and the output result SLEEC Data data : structure containing the complete dataset data.X : sparse n x d matrix containing train features data.Xt : sparse m x d matrix containing test features data.Y : sparse n x L matrix containing train labels data.Yt : sparse m x L matrix containing test labels SLEEC Parameters params.num_learners : number of SLEEC learners in ensemble (default 10) params.num_clusters : Initial number of clusters (default 300) params.num_threads : Number of threads for parallelization (default 32) params.SVP_neigh : Number of nearest neighbours to be preserved (default 15) params.out_Dim : embedding dimensions (default 50) params.w_thres : 1-w_thresh is the sparsity of regressors w (default 0.7) params.sp_thresh : 1-sp_thresh is the sparsity of embeddings (default 0.7) params.cost : liblinear cost coefficient (default 0.1) params.NNtest : number of nearest neighbours to consider while testing (default 10) params.normalize : 1 for normalized data, 2 for unnormalized data (only for mediamill) params.fname : filename for logging purposes SLEEC Result result.clusterCenters : cluster centers for the different learners result.tim_clus : time taken for clustering result.SVPModel : model for different learners containing embeddings and regressors result.SVPtime_mat : time taken for performing SVP for each learner result.regressiontime_mat : time taken for learning regressors result.precision : overall precision accuracy result.predictAcc : precision accuracy per test point result.predictLabels : Top-k labels predicted per test point result.tim_test : time taken for the testing procedure result.test_KNN : kNN Matrix for the test points Toy Example The zip file containing the source code also include the BibTeX dataset as a toy example. The following are the instructions to run SLEEC on the BibTeX dataset > cd Toy_Example > load bibtex.mat > bibtexParams > cd .. > [result] = SLEEC(data, params); To verify that you have run the code successfully, please compare the precision that you obtain with the result structure that has been provided in the Toy_Example folder.