Browsing by Supervisor "Pfahringer, Bernhard"
Now showing items 1-12 of 12
-
A study of self-training variants for semi-supervised image classification
(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
(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 ... -
Contextualised approaches to embedding word senses
(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 ... -
Domain-specific language models for multi-label classification of medical text
(The University of Waikato, 2022)Recent advancements in machine learning-based medical text multi-label classifications can be used to enhance the understanding of the human body and aid the need for patient care. This research considers predicting medical ... -
Efficient compilation of a verification-friendly programming language
(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 ... -
Heterogeneous Computing for Data Stream Mining
(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 ... -
Learning discrete and Lipschitz representations
(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 ... -
Meta-Learning and the Full Model Selection Problem
(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
(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 ... -
Scalable Multi-label Classification
(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 ... -
Sequence-based protein classification: binary Profile Hidden Markov Models and propositionalisation
(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 ... -
Tree-structured multiclass probability estimators
(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 ...
