The Wisdom of Crowds, an innovative theory described in social science, claims that the aggregate decisions made by a group will often be better than those of its individual members if the four fundamental criteria of this theory are satisfied. This theory used for in clustering problems. Previous researches showed that this theory can significantly increase the stability and performance of...

We propose a unified global entropy reduction maximization (GERM) framework for active learning and semi-supervised learning for speech recognition. Active learning aims to select a limited subset of utterances for transcribing from a large amount of un-transcribed utterances, while semi-supervised learning addresses the problem of selecting right transcriptions for un-transcribed utterances, s...

An adaptive Chinese hand-talking system is presented in this paper. By analyzing the 3 data collecting strategies for new users, the adaptation framework including supervised and unsupervised adaptation methods is proposed. For supervised adaptation, affinity propagation (AP) is used to extract exemplar subsets, and enhanced maximum a posteriori / vector field smoothing (eMAP/VFS) is proposed t...

Supervised interaction is concerned with the problem of establishing trust between contracting agents in electronic markets. It is designed to put safeguards in place that ensure that errant behaviour in business transactions is either prevented or sanctioned. Supervised Interaction consists of three elements: an organisational framework, a contract specification language and a contract managem...

Semi-supervised is the machine learning field. In the previous work, selection of pairwise constraints for semi-supervised clustering is resolved using active learning method in an iterative manner. Semi-supervised clustering derived from the pairwise constraints. The pairwise constraint depends on the two kinds of constraints such as must-link and cannot-link.In this system, enhanced iterative...

Semi-supervised clustering uses a small amount of supervised information to aid unsupervised learning. As one of the semi-supervised clustering methods, metric learning has been widely used to clustering the centralized data points. However, there are many distributed data points, which cannot be centralized for the various reasons. Based on MPCK-MEANS framework [1] , the method of distributed ...

The task of matching co-referent records is known among other names as record linkage. For large record-linkage problems, often there is little or no labeled data available, but unlabeled data shows a reasonably clear structure. For such problems, unsupervised or semi-supervised methods are preferable to supervised methods. In this paper, we describe a hierarchical graphical model framework for...

The discovery of interesting regions in spatial datasets is an important data mining task. In particular, we are interested in identifying disjoint, contiguous regions that are unusual with respect to the distribution of a given class; i.e. a region that contains an unusually low or high number of instances of a particular class. This paper centers on the discussion of techniques, methodologies...

We introduce a new paradigm to investigate unsupervised learning, reducing unsupervised learning to supervised learning. Specifically, we mitigate the subjectivity in unsupervised decision-making by leveraging knowledge acquired from prior, possibly heterogeneous, supervised learning tasks. We demonstrate the versatility of our framework via comprehensive expositions and detailed experiments on...

We propose a method for semi-supervised training of structured-output neural networks. Inspired by the framework of Generative Adversarial Networks (GAN), we train a discriminator network to capture the notion of a ‘quality’ of network output. To this end, we leverage the qualitative difference between outputs obtained on the labelled training data and unannotated data. We then use the discrimi...