Graph-based approaches have been successful in unsupervised and semi-supervised learning. In this paper, we focus on the real-world applications where the same instance can be represented by multiple heterogeneous features. The key point of utilizing the graph-based knowledge to deal with this kind of data is to reasonably integrate the different representations and obtain the most consistent m...

Semi-supervised learning (SSL) is focused on learning from labeled and unlabeled data by incorporating structural and statistical information of the available unlabeled data. The amount of data is dramatically increasing, but few of them are fully labeled, due to cost and time constraints. This is even more challenging for non-vectorial, proximity data, given by pairwise proximity values. Only ...

We consider the task of learning a classifier from the feature space X to the set of classes Y = {0, 1}, when the features can be partitioned into class-conditionally independent feature sets X 1 and X 2. We show the surprising fact that the class-conditional independence can be used to represent the original learning task in terms of 1) learning a classifier from X 2 to X 1 and 2) learning the...

The paper argues that a part of the current statistical discussion is not based on the standard firm foundations of the field. Among the examples we consider are prediction into the future, semi-supervised classification, and causality inference based on observational data.

Co-training is a well-known semi-supervised learning technique that applies two basic learners to train the data source, which uses the most confident unlabeled data to augment labeled data in the learning process. In the paper, we use the diversity of class probability estimation (DCPE) between two learners and propose the DCPE co-training approach. The key idea is to use DCPE to predict label...

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...