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Jan Feyereisl

  1. STORM - A Novel Information Fusion and Cluster Interpretation Technique.

    Пнд, 04/26/2010 - 15:01 — SomNambul
    Authors: Uwe Aickelin, Jan Feyereisl
    Subjects: Artificial Intelligence
    Abstract

    Analysis of data without labels is commonly subject to scrutiny by
    unsupervised machine learning techniques. Such techniques provide more
    meaningful representations, useful for better understanding of a problem at
    hand, than by looking only at the data itself. Although abundant expert
    knowledge exists in many areas where unlabelled data is examined, such
    knowledge is rarely incorporated into automatic analysis. Incorporation of
    expert knowledge is frequently a matter of combining multiple data sources from
    disparate hypothetical spaces.

  2. Detecting Motifs in System Call Sequences.

    Ср, 02/03/2010 - 16:01 — SomNambul
    Authors: Uwe Aickelin, Jan Feyereisl, William O. Wilson
    Subjects: Artificial Intelligence
    Abstract

    The search for patterns or motifs in data represents an area of key interest
    to many researchers. In this paper we present the Motif Tracking Algorithm, a
    novel immune inspired pattern identification tool that is able to identify
    unknown motifs which repeat within time series data. The power of the algorithm
    is derived from its use of a small number of parameters with minimal
    assumptions. The algorithm searches from a completely neutral perspective that
    is independent of the data being analysed, and the underlying motifs.

  3. Artificial Immune Tissue using Self-Orgamizing Networks.

    Чт, 10/29/2009 - 16:01 — SomNambul
    Authors: Uwe Aickelin, Jan Feyereisl
    Subjects: Artificial Intelligence
    Abstract

    As introduced by Bentley et al. (2005), artificial immune systems (AIS) are
    lacking tissue, which is present in one form or another in all living
    multi-cellular organisms. Some have argued that this concept in the context of
    AIS brings little novelty to the already saturated field of the immune inspired
    computational research. This article aims to show that such a component of an
    AIS has the potential to bring an advantage to a data processing algorithm in
    terms of data pre-processing, clustering and extraction of features desired by
    the immune inspired system.

Math-arch

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