We address two-dimensional shape-based classification, considering shapes described by arbitrary sets of unlabeled points, or landmarks. This is relevant in practice because, in m...
Many applications require a computer representation of 2D shape, usually described by a set of 2D points. The challenge of this representation is that it must not only capture the...
In the paper, we study the problem of optimal matching of two generalized functions (distributions) via a diffeomorphic transformation of the ambient space. In the particular case...
We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
In the context of binary classification, we define disagreement as a measure of how often two independently-trained models differ in their classification of unlabeled data. We exp...