Description: Transfer learning involves several critical issues and challenges: overfitting, under-
fitting, under-adaptation, and negative-transfer. Overfitting and underfitting may hap-
pen when modeling the unknown probability distribution based on observed data; Under-
adaptation and negative-transfer may happen when adapting the unknown probability dis-
tributions across domains: under-adaptation refers to the condition that the distribution
mismatch cannot be corrected sufficiently; negative-transfer refers to the condition that
the auxiliary task deteriorates the target task unintentionally. This thesis addresses the
underfitting, under-adaptation, and negative-transfer issues, analyzes the intrinsic causes,
and designs specific learning models
To Search:
File list (Check if you may need any files):
run_me_nn.m
run_me_svm.m
TSL_LRSR.m
Download