Now showing items 1-5 of 108

  • 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 ...
  • 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. ...
  • Bagging ensemble selection

    Sun, Quan; Pfahringer, Bernhard (Springer, 2011)
    Ensemble selection has recently appeared as a popular ensemble learning method, not only because its implementation is fairly straightforward, but also due to its excellent predictive performance on practical problems. The ...
  • Bagging ensemble selection for regression

    Sun, Quan; Pfahringer, Bernhard (Springer, 2012)
    Bagging ensemble selection (BES) is a relatively new ensemble learning strategy. The strategy can be seen as an ensemble of the ensemble selection from libraries of models (ES) strategy. Previous experimental results on ...

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

  • 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 ...
  • Meta-Learning and the Full Model Selection Problem

    Sun, Quan (University of Waikato, 2014)
    When working as a data analyst, one of my daily tasks is to select appropriate tools from a set of existing data analysis techniques in my toolbox, including data preprocessing, outlier detection, feature selection, learning ...
  • Policy Search Based Relational Reinforcement Learning using the Cross-Entropy Method

    Sarjant, Samuel (University of Waikato, 2013)
    Relational Reinforcement Learning (RRL) is a subfield of machine learning in which a learning agent seeks to maximise a numerical reward within an environment, represented as collections of objects and relations, by ...

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