| Experimental Results | These positive results are somewhat surprising since a very simple loss function was used on |
| Introduction | Instead of devising various techniques for coping with non-convex loss functions , we approach the problem from a different perspective. |
| Introduction | Although using a least squares loss function for classification appears misguided, there is a precedent for just this approach in the early pattern recognition literature (Duda et al., 2000). |
| Introduction | This loss function has the advantage that the entire training objective on both the labeled and unlabeled data now becomes convex, since it consists of a convex structured large margin loss on labeled data and a convex least squares loss on unlabeled data. |
| Semi-supervised Structured Large Margin Objective | The resulting loss function has a hat shape (usually called hat-loss), which is non-convex. |