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BieColl - Bielefeld Electronic Collections
Institutional Repository
786 Publications
More information
Detailed data provider information (OpenDOAR)


  • Invenio: A Modern Digital Library System

    Kaplun, Samuele (2010)
    Invenio is an integrated digital library system originally developed at CERN to run the CERN Document Server, currently one of the largest institutional repositories worldwide. It was started over 15 years ago and has been matured through many release cycles. Invenio is a GPL2 Open Source project based on an Apache/WSGI+Python+MySQL architecture. Its modular design enables it to serve a wide variety of usages, from a multimedia digital object repository, to a web journal, to a fully functi...

    Re-thinking Fedora's storage layer: A new high-level interface to remove old assumptions and allow novel use cases

    Birkland, Aaron; Blekinge, Asger Askov (2010)
    Traditionally, the pluggable storage interface in Fedora has followed a "low-level" paradigm where objects and datastreams are presented to the storage layer as independent, anonymous blobs of data. This arrangement has proven simple, reliable, and generally flexible. In the past few years however, there has been an increasing need for Fedora to mediate storage in more complex scenarios. Managing large numbers of big datastreams, multiplexing storage between different devices or cloud storage...

    Decision Manifolds: Classification Inspired by Self-Organization

    Pölzlbauer, Georg; Lidy, Thomas; Rauber, Andreas (2007)
    We present a classifier algorithm that approximates the decision surface of labeled data by a patchwork of separating hyperplanes. The hyperplanes are arranged in a way inspired by how Self-Organizing Maps are trained. We take advantage of the fact that the boundaries can often be approximated by linear ones connected by a low-dimensional nonlinear manifold. The resulting classifier allows for a voting scheme that averages over the classifiction results of neighboring hyperplanes. Our algorit...

    Ensemble Clustering Classification compete SVM and One-Class classifiers applied on plant microRNAs Data

    Yousef, Malik; Khalifa, Waleed; AbdAllah, Loai (2016)
    The performance of many learning and data mining algorithms depends critically on suitable metrics to assess efficiency over the input space. Learning a suitable metric from examples may, therefore, be the key to successful application of these algorithms. We have demonstrated that the k-nearest neighbor (kNN) classification can be significantly improved by learning a distance metric from labeled examples. The clustering ensemble is used to define the distance between points in respect to how...
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