Browsing by Author "Pfahringer, Bernhard"

Now showing items 1-5 of 117

  • 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.

  • Contextualised approaches to embedding word senses

    Ansell, Alan John (The University of Waikato, 2020)
    Vector representations of text are an essential tool for modern Natural Language Processing (NLP), and there has been much work devoted to finding effective methods for obtaining such representations. Most previously ...
  • 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 ...

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