Now showing items 1-20 of 30

  • Applying machine learning to programming by demonstration

    Paynter, Gordon W.; Witten, Ian H.; Koblitz, Neil; Powell, Matthew (Taylor & Francis, 2004)
    ‘Familiar’ is a tool that helps end-users automate iterative tasks in their applications by showing examples of what they want to do. It observes the user’s actions, predicts what they will do next, and then offers to ...
  • At the nexus of data and collections: new affordances in the age of mass-scale digital libraries

    Downie, J. Stephen; Lorang, Elizabeth; Soh, Leen-Kiat; Bainbridge, David; McIntyre, Sandra; Page, Kevin (ACM, 2018)
    Within the context of mass-scale digital libraries, this panel will explore methodologies and uses for-as well as the results of- conceiving of "data as collections" and "collections as data." The panel will explore the ...
  • Boosting trees for cost-sensitive classifications

    Ting, Kai Ming; Zheng, Zijian (University of Waikato, Department of Computer Science, 1998-01)
    This paper explores two boosting techniques for cost-sensitive tree classification in the situation where misclassification costs change very often. Ideally, one would like to have only one induction, and use the induced ...
  • A Comparison of Multi-instance Learning Algorithms

    Dong, Lin (The University of Waikato, 2006)
    Motivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard ...
  • Continuous Typist Verification using Machine Learning

    Hempstalk, Kathryn (The University of Waikato, 2009)
    A keyboard is a simple input device. Its function is to send keystroke information to the computer (or other device) to which it is attached. Normally this information is employed solely to produce text, but it can also ...
  • Data Quality in Predictive Toxicology: Identification of Chemical Structures and Calculation of Chemical Descriptors

    Helma, Christoph; Kramer, Stefan; Pfahringer, Bernhard; Gottmann, Eva (Environmental health perspectives, 2000)
    Every technique for toxicity prediction and for the detection of structure–activity relationships relies on the accurate estimation and representation of chemical and toxicologic properties. In this paper we discuss the ...
  • Effective Linear-Time Feature Selection

    Pradhananga, Nripendra (The University of Waikato, 2007)
    The classification learning task requires selection of a subset of features to represent patterns to be classified. This is because the performance of the classifier and the cost of classification are sensitive to the ...
  • Ensembles of nested dichotomies with multiple subset evaluation

    Leathart, Tim; Frank, Eibe; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2019)
    A system of nested dichotomies (NDs) is a method of decomposing a multiclass problem into a collection of binary problems. Such a system recursively applies binary splits to divide the set of classes into two subsets, and ...
  • Estimating replicability of classifier learning experiments

    Bouckaert, Remco R. (ACM, 2004)
    Replicability of machine learning experiments measures how likely it is that the outcome of one experiment is repeated when performed with a different randomization of the data. In this paper, we present an estimator of ...
  • Foreword: Special issue for the Journal Track of the 8th Asian Conference on Machine Learning (ACML 2016)

    Durrant, Robert J.; Kim, Kee-Eung; Holmes, Geoffrey; Marsland, Stephen; Sugiyama, Masashi; Zhou, Zhi-Hua (Springer, 2017)
    We, the guest editors, welcome you to this special issue of Machine Learning comprising papers accepted to the journal track of the 8th Asian conference on machine learning (ACML 2016), held at the University of Waikato, ...
  • Human-competitive automatic topic indexing

    Medelyan, Olena (The University of Waikato, 2009)
    Topic indexing is the task of identifying the main topics covered by a document. These are useful for many purposes: as subject headings in libraries, as keywords in academic publications and as tags on the web. Knowing a ...
  • Improving browsing in digital libraries with keyphrase indexes

    Gutwin, Carl; Paynter, Gordon W.; Witten, Ian H.; Nevill-Manning, Craig G.; Frank, Eibe (Elsevier Science B.V., 1999)
    Browsing accounts for much of people's interaction with digital libraries, but it is poorly supported by standard search engines. Conventional systems often operate at the wrong level, indexing words when people think in ...
  • Improving Hoeffding Trees

    Kirkby, Richard Brendon (The University of Waikato, 2007)
    Modern information technology allows information to be collected at a far greater rate than ever before. So fast, in fact, that the main problem is making sense of it all. Machine learning offers promise of a solution, but ...
  • Introduction: Special Issue of Selected Papers from ACML 2015

    Holmes, Geoffrey; Liu, Tie-Yan; Li, Hang; King, Irwin; Sugiyama, Masashi; Zhou, Zhi-Hua (2017)
    We are delighted to present this special issue of Machine Learning Journal with selected papers from the Seventh Asian Conference on Machine Learning (ACML 2015) held in Hong Kong, from 20 to 22 November 2015. ACML aims ...
  • An investigation into the use of machine learning for determining oestrus in cows

    Mitchell, R. Scott; Sherlock, Robert A.; Smith, Lloyd A. (University of Waikato, Department of Computer Science, 1995-08)
    A preliminary investigation of the application of two well-known machine learning schemes—C4.5 and FOIL—to detection of oestrus in dairy cows has been made. This is a problem of practical economic significance as each ...
  • Learning agents: from user study to implementation

    Maulsby, David; Witten, Ian H. (1996-04)
    Learning agents acquire procedures by being taught rather than programmed. To teach effectively, users prefer communicating in richer and more flexible ways than traditional computer dialogs allow. This paper describes the ...
  • Learning Instance Weights in Multi-Instance Learning

    Foulds, James Richard (The University of Waikato, 2008)
    Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug ...
  • Mapping vegetation with remote sensing and GIS data using object-based analysis and machine learning algorithms

    Pham, Thi Hong Lien (The University of Waikato, 2018)
    Remote sensing technology is an efficient tool for various practical applications of environmental resources management. Advances in this technology include the diverse range of high quality data sources and image analysis ...
  • An MDL estimate of the significance of rules

    Cleary, John G.; Legg, Shane; Witten, Ian H. (1996-03)
    This paper proposes a new method for measuring the performance of models-whether decision trees or sets of rules-inferred by machine learning methods. Inspired by the minimum description length (MDL) philosophy and ...
  • The need for open source software in machine learning

    Sonnenburg, Soren; Braun, Mikio L.; Ong, Cheng Soon; Bengio, Samy; Bottou, Leon; Holmes, Geoffrey; LeCunn, Yann; Muller, Klaus-Robert; Pereira, Fernando; Rasmussen, Carl Edward; Ratsch, Gunnar; Scholkopf, Bernhard; Smola, Alexander; Vincent, Pascal; Weston, Jason; Williamson, Robert C. (JMLR, 2007)
    Open source tools have recently reached a level of maturity which makes them suitable for building large-scale real-world systems. At the same time, the field of machine learning has developed a large body of powerful ...