15,101 research outputs found

    Concept discovery innovations in law enforcement: a perspective.

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    In the past decades, the amount of information available to law enforcement agencies has increased significantly. Most of this information is in textual form, however analyses have mainly focused on the structured data. In this paper, we give an overview of the concept discovery projects at the Amsterdam-Amstelland police where Formal Concept Analysis (FCA) is being used as text mining instrument. FCA is combined with statistical techniques such as Hidden Markov Models (HMM) and Emergent Self Organizing Maps (ESOM). The combination of this concept discovery and refinement technique with statistical techniques for analyzing high-dimensional data not only resulted in new insights but often in actual improvements of the investigation procedures.Formal concept analysis; Intelligence led policing; Knowledge discovery;

    A survey of machine learning techniques applied to self organizing cellular networks

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    In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

    Neural Networks for Complex Data

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    Artificial neural networks are simple and efficient machine learning tools. Defined originally in the traditional setting of simple vector data, neural network models have evolved to address more and more difficulties of complex real world problems, ranging from time evolving data to sophisticated data structures such as graphs and functions. This paper summarizes advances on those themes from the last decade, with a focus on results obtained by members of the SAMM team of Universit\'e Paris

    Efficient Recognition of authentic dynamic facial expressions on the FEEDTUM database

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    In order to allow for fast recognition of a user’s affective state we discuss innovative holistic and self organizing approaches for efficient facial expression analysis. The feature set is thereby formed by global descriptors and MPEG based DCT coefficients. In view of subsequent classification we compare modelling by pseudo multidimensional Hidden Markov Models and Support Vector Machines. Within the latter case super-vectors are constructed based on Sequential Floating Search Methods. Extensive test-runs as a proof of concept are carried out on our publicly available FEEDTUM database consisting of elicited spontaneous emotions of 18 subjects within the MPEG-4 emotion-set plus added neutrality. Maximum recognition performance reaches the benchmark-rate gained by a human perception test with 20 test-persons and manifest the effectiveness of the introduced novel concepts. 1
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