brain mr images tissue segmentation is one of the most important parts of the clinical diagnostic tools. pixel classification methods have been frequently used in the image segmentation with two supervised and unsupervised approaches up to now. supervised segmentation methods lead to high accuracy but they need a large amount of labeled data, which is hard, expensive and slow to obtain. moreove...

Stance detection is the task of automatically determining the author’s favorability towards a given target. However, the target may not be explicitly mentioned in the text and even someone may refer some positive opinions to against the target, which make the task more difficult. In this paper, we describe an ensemble framework which integrates various feature sets and classification methods, a...

In this paper, we present a new analysis on co-training, a representative paradigm of disagreement-based semi-supervised learning methods. In our analysis the co-training process is viewed as a combinative label propagation over two views; this provides a possibility to bring the graph-based and disagreementbased semi-supervised methods into a unified framework. With the analysis we get some in...

We present a general framework of spectral methods for semi-supervised dimensionality reduction. Applying an approach called manifold regularization, our framework naturally generalizes existent supervised frameworks. Furthermore, by our two semi-supervised versions of the representer theorem, our framework can be kernelized as well. Using our framework, we give three examples of semi-supervise...

The spread of misinformation in social media outlets has become a prevalent societal problem and is the cause many kinds unrest. Curtailing its prevalence great importance machine learning shown significant promise. However, there are two main challenges when applying to this problem. First, while much too one respect, misinformation, actually, represents only minor proportion all postings seen...

In this paper, we propose an effective fusion scheme of visual and auditory modalities to detect events in sports video. The proposed scheme is built upon semantic shot classification, where we classify video shots into several major or interesting classes, each of which has clear semantic meanings. Among major shot classes we perform classification of the different auditory signal segments (i....

Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive obtain. Recently, methods for semi-supervised learning proposed and achieved excellent performance. In this study, we propose a new EnAET framework further improve existing with self-supervised information. To our best knowl...

A novel framework called Graph diffusion & PCA (GDPCA) is proposed in the context of semi-supervised learning on graph structured data. It combines a modified Principal Component Analysis with classical supervised loss and Laplacian regularization, thus handling case where adjacency matrix Sparse avoiding Curse dimensionality. Our can be applied to non-graph datasets as well, such images by con...