In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assum...
Abstract— This paper proposes an approach to learn subjectindependent P300 models for EEG-based brain-computer interfaces. The P300 models are first learned using a pool of exis...
We consider the problem of Semi-supervised Learning (SSL) from general unlabeled data, which may contain irrelevant samples. Within the binary setting, our model manages to better...
Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. L...
In this paper, we design a local classification algorithm using implicit link analysis, considering the situation that the labeled and unlabeled data are drawn from two different ...
Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, inc...
In this paper we propose to study budget semi-supervised learning, i.e., semi-supervised learning with a resource budget, such as a limited memory insufficient to accommodate and/...
Zhi-Hua Zhou, Michael Ng, Qiao-Qiao She, Yuan Jian...
Abstract. Semi-supervised learning and ensemble learning are two important learning paradigms. The former attempts to achieve strong generalization by exploiting unlabeled data; th...
The annotation of words and phrases by ontology concepts is extremely helpful for semantic interpretation. However many ontologies, e.g. WordNet, are too fine-grained and even hu...
An adaptive semi-supervised ensemble method, ASSEMBLE, is proposed that constructs classification ensembles based on both labeled and unlabeled data. ASSEMBLE alternates between a...
Traditional spectral classification has been proved to be effective in dealing with both labeled and unlabeled data when these data are from the same domain. In many real world ap...