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Proactive anomaly detection refers to anticipating anomalies or abnormal patterns within a dataset in a timely manner. Discovering anomalies such as failures or degradations before their occurrence can lead to great benefits such as the ability to avoid the anomaly happening by applying some corrective measures in advance (e.g., allocating more resources for a nearly saturated system in a data centre)...
Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using term frequency vectors to represent microblog...
Data mining is a challenge for end-users, which requires knowledge and skills on business domains, data mining algorithms and software development. In response to the challenge, we have proposed, designed and implemented a novel data mining system named RFDM (RHadoop-based Fuzzy Data Mining), which supports fuzzy data mining process and experience with user convenience and reduced cost. The system...
Mining the large volume textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristic, finding proper representations of microblog texts is nontrivial. In this paper, we propose to build deep network-based...
With the popularity of social network and the increasing number of Web Services, making individual service recommendation has been a hot research spot nowadays. In this paper, we present a service recommendation algorithm named as URPC-Rec (User Relationships & Preferences Clustering and Recommendation), which first clusters users based on their history behaviors such as the services they...
In this paper, a modified Neural Gas algorithm is proposed and used to approximate hand topology. As original Neural Gas algorithm is intractable for real-time applications, some optimization such as unnecessary adaption removal and simple learning rate function are introduced to make it applicable for real-time applications. With segmented hand area, the topology representation can be obtained based...
With the development of computer aided design technique,3D reconstruction from 2D input has been widely used in the research of computer vision. Plentiful works have been done in three orthographic views, in this paper a new method is introduced under single 2D input. The system in this paper aims at the reconstruction of useful objects in computer design. Angle histogram and corner detector are used...
This paper investigates the Bayesian Ying-Yang (BYY) learning for speech recognition via Gaussian mixture models (GMMs) based Hidden Markov models (HMMs). A two level procedure is proposed with the hidden Markov level trained still under the maximum likelihood principle by the Baum-Welch algorithm but with the GMMs level trained under the BYY best harmony. We proposed a new batch way EM-like Ying-Yang...
Multiple-instance learning is a special machine learning algorithm between supervised learning and unsupervised learning, which has been used in medicine design, image retrieval and other research fields, and attained good performance. Diverse Density (DD) algorithm is a typical multiple- instance learning method. Due to the character of sparse positive instances, when classifying the bags which include...
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