We present a clustered personal classifier method (CPC method) that jointly estimates a classifier and clusters of workers in order to address the learning from crowds problem. Crowdsourcing allows us to create a large but low-quality data set at very low cost. The learning from crowds problem is to learn a classifier from such a lowquality data set. From some observations, we notice that worke...

Competitive learning mechanisms for clustering in general suffer from poor performance for very high dimensional ( ) data because of “curse of dimensionality” effects. In applications such as document clustering, it is customary to normalize the high dimensional input vectors to unit length, and it is sometimes also desirable to obtain balanced clusters, i.e., clusters of comparable sizes. The ...

The central idea of this paper is to comprehend knowledge integration in inter-organizational IS projects through a knowledge cluster view of inter-organizational IS projects. The proliferation of such projects combined with the complexity of managing them, motivated this study. The study is based on a single case study which involves four organizations collaborating on a project. Findings sugg...

This study accounts for the acquisition of the consonant clusters of English syllable structures both in onset and coda positions by Persian EFL learners. Persian syllable structure is "CV(CC)", composed of one consonant at the initial position and two optional consonants at the final position; whereas English syllable structure is "(CCC)V(CCCC)". Therefore, Persian EFL learners need to resolve...

In the paper, we use ontology technology to construct a web courseware authoring system and realize the semantic interoperation in heterogeneous e-learning systems. We set up a domain ontology model which is core component of the system, and can be related with the knowledge concept in domain ontology base with learning resources in the learning resources repository. Then we use the SCORM simpl...

We propose multitask Laplacian learning, a new method for jointly learning clusters of closely related tasks. Unlike standard multitask methodologies, the graph of relations among the tasks is not assumed to be known a priori, but is learned by the multitask Laplacian algorithm. The algorithm builds on kernel based methods and exploits an optimization approach for learning a continuously parame...

By using multi-agents system model simulation method, this paper tries to reveal the key driving force for the evolution of industrial clusters under the dynamic development of the external environment and internal innovation. Also, how important role of policy recommendations is proved for the regional industrial development. For the purpose of studying the evolution of industrial clusters, a ...