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Automatic Term Extraction is an important issue in Natural Language Processing. This paper presents a new approach of terminology extraction combining with machine learning based on cascaded conditional random fields and corpus-based statistical model. In this approach, firstly, the low-layer and high-layer conditional random fields (CRFs) are used to extract the simple and compound terminologies...
This work addresses the problem of learning an unknown function from data when local models are employed. In particular, kernel smoothing models are considered, which use kernels in a straightforward fashion by modeling the output as a weighted average of values observed in a neighborhood of the input. Such models are a popular alternative to other kernel paradigms, such as support vector machines...
Classification of roadside objects is very important task in identifying fire risk regions, analysing roadside conditions and improving roadside safety. This paper introduces a novel and effective way to detect soil, grass, road and tree from roadside images thus giving a better decision-making system for analysing roadside video data. A new feature extraction approach is proposed to detect and classify...
Migraine is a chronic neurological disease characterized by recurrent moderate to severe headaches during a period like one month often in association with symptoms in human brain and autonomic nervous system. Normally, migraine symptoms can be categorized into four different stages: inter-ictal, pre-ictal, ictal, and post-ictal stages. Since migraine patients are difficulty knowing when they will...
In this paper we study a new problem of online diffusion source detection in social networks. Existing work on diffusion source detection focuses on offline learning, which assumes data collected from network detectors are static and a snapshot of network is available before learning. However, an offline learning model does not meet the needs of early warning, real-time awareness, and real-time response...
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