Finding discriminative motifs has recently received much attention in biomedicine as such motifs allow us to characterize in distinguishing two different classes of sequences. It is common in biomedical applications that the quantity of labeled sequences is very limited while a large number of unlabeled sequences is usually available. The current methods of discriminative motif finding are powe...

It is a challenging vision problem to discover non-rigid shape deformation for an image ensemble belonging to a single object class, in an automatic or semi-supervised fashion. The conventional semi-supervised approach [1] uses a congealing-like process to propagate manual landmark labels from a few images to a large ensemble. Although effective on an inter-person database with a large populati...

In recent years, semi-supervised learning has been a hot research topic in machine learning area. Different from traditional supervised learning which learns only from labeled data; semi-supervised learning makes use of both labeled and unlabeled data for learning purpose. Co-training is a popular semi-supervised learning algorithm which assumes that each example is represented by two or more r...

exploiting multimodal information like acceleration and heart rate is a promising method to achieve human action recognition. a semi-supervised action recognition approach aucc (action understanding with combinational classifier) using the diversity of base classifiers to create a high-quality ensemble for multimodal human action recognition is proposed in this paper. furthermore, both labeled ...

An ensemble is a group of learning models that jointly solve a problem. However, the ensembles generated by existing techniques are sometimes unnecessarily large, which can lead to extra memory usage, computational costs, and occasional decreases in effectiveness. The purpose of ensemble pruning is to search for a good subset of ensemble members that performs as well as, or better than, the ori...

Entity alignment is to find identical entities in different knowledge graphs. Although embedding-based entity has recently achieved remarkable progress, training data insufficiency remains a critical challenge. Conventional semi-supervised methods also suffer from the incorrect newly proposed data. To resolve these issues, we design an iterative cycle-teaching framework for alignment. The key i...