Now showing items 1-5 of 91

  • 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 ...
  • Batch-incremental versus instance-incremental learning in dynamic and evolving data

    Read, Jesse; Bifet, Albert; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2012)
    Many real world problems involve the challenging context of data streams, where classifiers must be incremental: able to learn from a theoretically- infinite stream of examples using limited time and memory, while being ...

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

  • 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 ...
  • Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation

    Mutter, Stefan (University of Waikato, 2011)
    Detecting similarity in biological sequences is a key element to understanding the mechanisms of life. Researchers infer potential structural, functional or evolutionary relationships from similarity. However, the concept ...
  • Scalable Multi-label Classification

    Read, Jesse (University of Waikato, 2010)
    Multi-label classification is relevant to many domains, such as text, image and other media, and bioinformatics. Researchers have already noticed that in multi-label data, correlations exist between labels, and a variety ...