Browsing by Author "Trigg, Leonard E."

Now showing items 1-5 of 8

  • Data mining in bioinformatics using Weka

    Frank, Eibe; Hall, Mark A.; Trigg, Leonard E.; Holmes, Geoffrey; Witten, Ian H. (Oxford University Press., 2004)
    The Weka machine learning workbench provides a general purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an ...
  • Designing similarity functions

    Trigg, Leonard E. (The University of Waikato, 1997)
    The concept of similarity is important in many areas of cognitive science, computer science, and statistics. In machine learning, functions that measure similarity between two instances form the core of instance-based ...
  • A diagnostic tool for tree based supervised classification learning algorithms

    Holmes, Geoffrey; Trigg, Leonard E. (1999-03)
    The process of developing applications of machine learning and data mining that employ supervised classification algorithms includes the important step of knowledge verification. Interpretable output is presented to a user ...
  • An entropy gain measure of numeric prediction performance

    Trigg, Leonard E. (University of Waikato, Department of Computer Science, 1998-05)
    Categorical classifier performance is typically evaluated with respect to error rate, expressed as a percentage of test instances that were not correctly classified. When a classifier produces multiple classifications for ...
  • Experiences with a weighted decision tree learner

    Cleary, John G.; Trigg, Leonard E. (University of Waikato, Department of Computer Science, 1998-05)
    Machine learning algorithms for inferring decision trees typically choose a single “best” tree to describe the training data. Recent research has shown that classification performance can be significantly improved by voting ...