Browsing by Subject "Machine Learning"

Now showing items 1-9 of 9

  • Introducing machine learning concepts with WEKA.

    Smith, Tony C.; Frank, Eibe (2016)
    This chapter presents an introduction to data mining with machine learning. It gives an overview of various types of machine learning, along with some examples. It explains how to download, install, and run the WEKA data ...
  • 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 ...
  • Linear Genetic Programming with Experience

    Liu, Liang (University of Waikato, 2015)
    A novel method of using Machine Learning (ML) algorithms to improve the performance of Linear Genetic Programming (LGP) is studied. In this study, structures used to organize the trained ML models are called Experience ...
  • Machine Learning for Adaptive Computer Game Opponents

    Miles, Jonathan David (The University of Waikato, 2009)
    This thesis investigates the use of machine learning techniques in computer games to create a computer player that adapts to its opponent's game-play. This includes first confirming that machine learning algorithms can ...
  • Mana Motuhake Ringa: The non-invasive neural interface based artificial hand

    Owen, Mahonri William (The University of Waikato, 2019)
    Ten million people on the earth at any given time suffer from the loss or lack of a limb. Three million of these are upper extremity amputees. No matter the cause or reason for amputation the amputees’ life is never the ...
  • 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 ...
  • 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 ...
  • 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 ...
  • 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 ...