Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

Browse

Filters

2014 - 20152

Settings

Publication Index: search.filters.publicationIndex.WoSSubject: search.filters.subject.Classification

Search Results

Now showing 1 - 2 of 2
  • Book Part
    Citation - WoS: 299
    Citation - Scopus: 406
    Introduction To Machine Learning
    (Humana Press, 2014) Baştanlar, Yalın; Özuysal, Mustafa
    The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.
  • Article
    Citation - WoS: 24
    Citation - Scopus: 31
    Dynamic Replication Strategies in Data Grid Systems: A Survey
    (Springer Verlag, 2015) Tos, Uras; Mokadem, Riad; Hameurlain, Abdelkader; Ayav, Tolga; Bora, Şebnem
    In data grid systems, data replication aims to increase availability, fault tolerance, load balancing and scalability while reducing bandwidth consumption, and job execution time. Several classification schemes for data replication were proposed in the literature, (i) static vs. dynamic, (ii) centralized vs. decentralized, (iii) push vs. pull, and (iv) objective function based. Dynamic data replication is a form of data replication that is performed with respect to the changing conditions of the grid environment. In this paper, we present a survey of recent dynamic data replication strategies. We study and classify these strategies by taking the target data grid architecture as the sole classifier. We discuss the key points of the studied strategies and provide feature comparison of them according to important metrics. Furthermore, the impact of data grid architecture on dynamic replication performance is investigated in a simulation study. Finally, some important issues and open research problems in the area are pointed out.