文件名称:MTBoost1
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Duchi and Singer [24] proposed a boosting method for multi-class classification problems
by utilizing the structural sparsity of model parameters. They claimed that the
method can be generalized for multi-task learning.
by utilizing the structural sparsity of model parameters. They claimed that the
method can be generalized for multi-task learning.
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下载文件列表
MTBoost1/Base_Learner_Test.m
MTBoost1/Base_Learner_Train.m
MTBoost1/dataset.txt
MTBoost1/Init.m
MTBoost1/loss_derivative.m
MTBoost1/MTBoost.m
MTBoost1/PreprocessMTData.m
MTBoost1/readme.txt
MTBoost1/WeightedRR.m
MTBoost1
MTBoost1/Base_Learner_Train.m
MTBoost1/dataset.txt
MTBoost1/Init.m
MTBoost1/loss_derivative.m
MTBoost1/MTBoost.m
MTBoost1/PreprocessMTData.m
MTBoost1/readme.txt
MTBoost1/WeightedRR.m
MTBoost1
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