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Experiments for "Multi-Class Learning using Unlabeled Samples: Theory and Algorithm"
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README.md

README.md

Multi-Class Learning using Unlabeled Samples: Theory and Algorithm

Code for experiments in "Multi-Class Learning using Unlabeled Samples: Theory and Algorithm". The paper has been accepted by IJCAI-19.

Structure

  • ./data/: Store parameter tuning results.
  • ./datasets/: Store primal libsvm style datasets. All datasets are available in LibSVM Data.
  • ./result/: Store final results for experiments.
  • ./libsvm/: Libvm tools used in codes, including lisvmread, which is provided in libsvm.
  • ./code/core_functions: Core functions used in expereiments, including the proposed algorithm, cross validition and so on.
  • ./code/utils/: Common utils used in experiments.
  • ./code/tune_parameters.m: Tune optimal parameters set and save them.
  • ./code/parameter_observe.m: Load parameter tuning results and choose the best one.
  • ./code/experiment_*.m: Scripts for experiments.

Steps

Just run experiment_*.m individually.

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