Sökning: "existence for large data"

Visar resultat 1 - 5 av 149 avhandlingar innehållade orden existence for large data.

  1. 1. Finding, extracting and exploiting structure in text and hypertext

    Författare :Ola Ågren; Jürgen Börstler; Frank Drewes; Maarten de Rijke; Umeå universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Automatic propagation; CHiC; Data mining; Discrete data; Extraction; Hierarchies; ProT; Rank distribution; S²ProT; Spatial linking; Web mining; Web searching; Computer science; Datalogi; business data processing; administrativ databehandling;

    Sammanfattning : Data mining is a fast-developing field of study, using computations to either predict or describe large amounts of data. The increase in data produced each year goes hand in hand with this, requiring algorithms that are more and more efficient in order to find interesting information within a given time. LÄS MER

  3. 2. Automated Derivation of Random Generators for Algebraic Data Types

    Författare :Claudio Agustin Mista; Chalmers tekniska högskola; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; Random Testing; Penetration Testing; Meta-programming; Haskell;

    Sammanfattning : Many testing techniques such as generational fuzzing or random property-based testing require the existence of some sort of random generation process for the values used as test inputs. Implementing such generators is usually a task left to end-users, who do their best to come up with somewhat sensible implementations after several iterations of trial and error. LÄS MER

  4. 3. High-Performance Computing For Support Vector Machines

    Författare :Shirin Tavara; Alexander Schliep; Alexander Karlsson; Richard Johansson; Högskolan i Skövde; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; Skövde Artificial Intelligence Lab SAIL ; Skövde Artificial Intelligence Lab SAIL ; INF301 Data Science; INF301 Data Science;

    Sammanfattning : Machine learning algorithms are very successful in solving classification and regression problems, however the immense amount of data created by digitalization slows down the training and predicting processes, if solvable at all. High-Performance Computing(HPC) and particularly parallel computing are promising tools for improving the performance of machine learning algorithms in terms of time. LÄS MER

  5. 4. Distributed and federated learning of support vector machines and applications

    Författare :Shirin Tavara; Alexander Schliep; Alexander Karlsson; Lili Jiang; Högskolan i Skövde; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Skövde Artificial Intelligence Lab SAIL ; Skövde Artificial Intelligence Lab SAIL ;

    Sammanfattning : Machine Learning (ML) has achieved remarkable success in solving classification, regression, and related problems over the past decade. In particular the exponential growth of digital data, makes using ML inevitable and necessary to exploit the wealth of information hidden inside the data. LÄS MER

  7. 5. Quasi-Arithmetic Filters for Topology Optimization

    Författare :Linus Hägg; Martin Berggren; Eddie Wadbro; Umeå universitet; []
    Nyckelord :NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES;

    Sammanfattning : Topology optimization is a framework for finding the optimal layout of material within a given region of space. In material distribution topology optimization, a material indicator function determines the material state at each point within the design domain. LÄS MER