Browsing by Supervisor "Frank, Eibe"

Now showing items 1-10 of 10

  • A study of self-training variants for semi-supervised image classification

    Sahito, Attaullah (The University of Waikato, 2021)
    Artificial neural networks achieve state-of-the-art performance when trained on a vast number of labelled examples. Still, they can easily overfit training examples when few labelled examples are available. The requirement ...
  • 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 ...
  • Concept-based Text Clustering

    Huang, Lan (University of Waikato, 2011)
    Thematic organization of text is a natural practice of humans and a crucial task for today's vast repositories. Clustering automates this by assessing the similarity between texts and organizing them accordingly, grouping ...
  • High-throughput machine learning algorithms

    Mitchell, Rory (The University of Waikato, 2021)
    The field of machine learning has become strongly compute driven, such that emerging research and applications require larger amounts of specialised hardware or smarter algorithms to advance beyond the state-of-the-art. ...
  • 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 ...
  • 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 ...
  • Smoothing in Probability Estimation Trees

    Han, Zhimeng (University of Waikato, 2011)
    Classification learning is a type of supervised machine learning technique that uses a classification model (e.g. decision tree) to predict unknown class labels for previously unseen instances. In many applications it can ...
  • Super-resolution of satellite imagery

    Bull, Daniel (The University of Waikato, 2021)
    Can deep neural networks super-resolve satellite imagery to a high perceptual quality? This thesis explores the juxtaposition between the pixel accuracy and perceptual qualities of super-resolved imagery by comparing and ...
  • 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 ...
  • 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 ...