Browsing by Author "Pfahringer, Bernhard"

Now showing items 1-5 of 113

  • Adaptive random forests for evolving data stream classification

    Gomes, Heitor Murilo; Bifet, Albert; Read, Jesse; Barddal, Jean Paul; Enembreck, Fabrício; Pfahringer, Bernhard; Holmes, Geoffrey; Abdessalem, Talel (Springer, 2017)
    Random forests is currently one of the most used machine learning algorithms in the non-streaming (batch) setting. This preference is attributable to its high learning performance and low demands with respect to input ...
  • AffectiveTweets: a Weka package for analyzing affect in tweets

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard; Mohammad, Saif M. (Microtome Publishing, 2019)
    AffectiveTweets is a set of programs for analyzing emotion and sentiment of social media messages such as tweets. It is implemented as a package for the Weka machine learning workbench and provides methods for calculating ...
  • Algorithm selection on data streams

    van Rijn, Jan N.; Holmes, Geoffrey; Pfahringer, Bernhard; Vanschoren, Joaquin (Springer International Publishing, 2014)
    We explore the possibilities of meta-learning on data streams, in particular algorithm selection. In a first experiment we calculate the characteristics of a small sample of a data stream, and try to predict which classifier ...
  • Annotate-Sample-Average (ASA): A New Distant Supervision Approach for Twitter Sentiment Analysis

    Bravo-Marquez, Felipe; Frank, Eibe; Pfahringer, Bernhard (IOS Press, 2016-01-01)
    The classification of tweets into polarity classes is a popular task in sentiment analysis. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. ...
  • Automatic end-to-end De-identification: Is high accuracy the only metric?

    Yogarajan, Vithya; Pfahringer, Bernhard; Mayo, Michael (2019)
    De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data. It is a two-step process where step one is the identification of ...

Showing up to 5 theses - most recently added to Research Commons first.

  • Learning discrete and Lipschitz representations

    Gouk, Henry (The University of Waikato, 2019)
    Learning to embed data into a low dimensional vector space that is more useful for some downstream task is one of the most common problems addressed in the representation learning literature. Conventional approaches to ...
  • Tree-structured multiclass probability estimators

    Leathart, Timothy Matthew (The University of Waikato, 2019)
    Nested dichotomies are used as a method of transforming a multiclass classification problem into a series of binary problems. A binary tree structure is constructed over the label space that recursively splits the set of ...
  • Efficient compilation of a verification-friendly programming language

    Weng, Min-Hsien (The University of Waikato, 2019)
    This thesis develops a compiler to convert a program written in the verification friendly programming language Whiley into an efficient implementation in C. Our compiler uses a mixture of static analysis, run-time monitoring ...
  • Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis

    Bravo-Marquez, Felipe (University of Waikato, 2017)
    The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on ...
  • Heterogeneous Computing for Data Stream Mining

    Petko, Vladimir (University of Waikato, 2016)
    Graphical Processing Units are de-facto standard for acceleration of data parallel tasks in high performance computing. They are widely used to accelerate batch machine learning algorithms. High-end discrete GPUs are ...

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