Browsing by Author "Frank, Eibe"

Now showing items 1-5 of 104

  • Accelerating the XGBoost algorithm using GPU computing

    Mitchell, Rory; Frank, Eibe (2017)
    We present a CUDA-based implementation of a decision tree construction algorithm within the gradient boosting library XGBoost. The tree construction algorithm is executed entirely on the graphics processing unit (GPU) and ...
  • Accurate photometric redshift probability density estimation - method comparison and application

    Rau, Michael M.; Seitz, Stella; Frank, Eibe; Brimioulee, Fabrice; Friedrich, Oliver; Gruen, Daniel; Hoyle, Ben (Oxford University Press (OUP): Policy P - Oxford Open Option A, 2015)
    We introduce an ordinal classification algorithm for photometric redshift estimation, which significantly improves the reconstruction of photometric redshift probability density functions (PDFs) for individual galaxies and ...
  • Active learning of soft rules for system modelling

    Frank, Eibe; Huber, Klaus-Perter (1996)
    Using rule learning algorithms to model systems has gained considerable interest in the past. The underlying idea of active learning is to learning algorithm influence the selection of training examples. The presented ...
  • Additive Regression Applied to a Large-Scale Collaborative Filtering Problem

    Frank, Eibe; Hall, Mark A. (Springer, 2008)
    The much-publicized Netflix competition has put the spotlight on the application domain of collaborative filtering and has sparked interest in machine learning algorithms that can be applied to this sort of problem. The ...
  • 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 ...

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 ...
  • 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 ...
  • Parameter Tuning Using Gaussian Processes

    Ma, Jinjin (University of Waikato, 2012)
    Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter ...
  • Using Output Codes for Two-class Classification Problems

    Zeng, Fanhua (University of Waikato, 2011)
    Error-correcting output codes (ECOCs) have been widely used in many applications for multi-class classification problems. The problem is that ECOCs cannot be ap- plied directly on two-class datasets. The goal of this thesis ...

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