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...

Imagine two identical people receive exactly the same training on how to classify certain objects. Perhaps surprisingly, we show that one can then manipulate them into classifying some test items in opposite ways, simply depending on what other test items they are asked to classify (without label feedback). We call this the Test-Item Effect, which can be induced by the order or the distribution...

Automated short answer scoring is increasingly used to give students timely feedback about their learning progress. Building scoring models comes with high costs, as stateof-the-art methods using supervised learning require large amounts of hand-annotated data. We analyze the potential of recently proposed methods for semi-supervised learning based on clustering. We find that all examined metho...

Blog classification (e.g., identifying bloggers’ gender or age) is one of the most interesting current problems in blog analysis. Although this problem is usually solved by applying supervised learning techniques, the large labeled dataset required for training is not always available. In contrast, unlabeled blogs can easily be collected from the web. Therefore, a semi-supervised learning metho...

Graph-based semi-supervised learning (GSSL) is one of the most important semi-supervised learning (SSL) paradigms. Though GSSL methods are helpful in many situations, they may hurt performance when using unlabeled data. In this paper, we propose a new GSSL method GsslIs based on instance selection in order to reduce the chances of performance degeneration. Our basic idea is that given a set of ...

Semi-supervised learning (SSL) concerns how to improve performance via the usage of unlabeled data. Recent studies indicate that the usage of unlabeled data might even deteriorate performance. Although some proposals have been developed to alleviate such a fundamental challenge for semisupervised classification, the efforts on semi-supervised regression (SSR) remain to be limited. In this work ...

If we know most of Smith’s friends are from Boston, what can we say about the rest of Smith’s friends? In this paper, we focus on the node classification problem on networks, which is one of the most important topics in AI and Web communities. Our proposed algorithm which is referred to as OMNIProp has the following properties: (a) seamless and accurate; it works well on any label correlations ...

Some languages do not have enough labeled data to obtain good discourse parsing, specially in the relation identification step, and the additional use of unlabeled data is a plausible solution. A workflow is presented that uses a semi-supervised learning approach. Instead of only a predefined additional set of unlabeled data, texts obtained from the web are continuously added. This obtains near...