Unconstrained large margin distribution machines (ULDMs) maximize the margin mean and minimize the margin variance without constraints. In this paper, we first reformulate ULDMs as a special case of least squares (LS) LDMs, which are a least squares version of LDMs. By setting a hyperparameter to control the trade-off between the generalization ability and the training error to zero, LS LDMs reduce to ULDMs. In the computer experiments, we include the zero value of the hyperparameter as a candidate value for model selection. According to the experiments using two-class problems, in most cases LS LDMs reduce to ULDMs and their generalization abilities are comparable. Therefore, ULDMs are sufficient to realize high generalization abilities without equality constraints.
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