Browsing by Subject "machine learning"

Now showing items 21-33 of 33

  • 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 ...
  • On calibration of nested dichotomies

    Leathart, Tim; Frank, Eibe; Pfahringer, Bernhard; Holmes, Geoffrey (Springer, 2019)
    Nested dichotomies (NDs) are used as a method of transforming a multiclass classification problem into a series of binary problems. A tree structure is induced that recursively splits the set of classes into subsets, and ...
  • Practical feature subset selection for machine learning

    Hall, Mark A.; Smith, Lloyd A. (Springer, 1998)
    Machine learning algorithms automatically extract knowledge from machine readable information. Unfortunately, their success is usually dependant on the quality of the data that they operate on. If the data is inadequate, ...
  • Prediction Intervals for Class Probabilities

    Yu, Xiaofeng (The University of Waikato, 2007)
    Prediction intervals for class probabilities are of interest in machine learning because they can quantify the uncertainty about the class probability estimate for a test instance. The idea is that all likely class probability ...
  • Prediction of Oestrus in Dairy Cows: An Application of Machine Learning to Skewed Data

    Lynam, Adam David (The University of Waikato, 2009)
    The Dairy industry requires accurate detection of oestrus(heat) in dairy cows to maximise output of the animals. Traditionally this is a process dependant on human observation and interpretation of the various signs of ...
  • Sampling-based Prediction of Algorithm Runtime

    Sun, Quan (The University of Waikato, 2009)
    The ability to handle and analyse massive amounts of data has been progressively improved during the last decade with the growth of computing power and the opening up of the Internet era. Nowadays, machine learning algorithms ...
  • Scalable Text Mining with Sparse Generative Models

    Puurula, Antti (University of Waikato, 2015)
    The information age has brought a deluge of data. Much of this is in text form, insurmountable in scope for humans and incomprehensible in structure for computers. Text mining is an expanding field of research that seeks ...
  • Scientific workflow management with ADAMS

    Reutemann, Peter; Vanschoren, Joaquin (Springer, 2012)
    We demonstrate the Advanced Data mining And Machine learning System (ADAMS), a novel workflow engine designed for rapid prototyping and maintenance of complex knowledge workflows. ADAMS does not require the user to manually ...
  • Selected data exploration methods in hydroclimatology

    Vetrova, Varvara (University of Waikato, 2016)
    The volumes of climatological data are rapidly growing due to development of new acquisition platforms and advances in data storage technologies. Such advances provide new challenging problems for data analysis methods. ...
  • Statistical Learning in Multiple Instance Problems

    Xu, Xin (The University of Waikato, 2003)
    Multiple instance (MI) learning is a relatively new topic in machine learning. It is concerned with supervised learning but differs from normal supervised learning in two points: (1) it has multiple instances in an example ...
  • Subset selection using rough numeric dependency

    Smith, Tony C.; Holmes, Geoffrey (University of Waikato, Department of Computer Science, 1995-04)
    In this paper we describe a novel method for performing feature subset selection for supervised learning tasks based on a refined notion of feature relevance. We define relevance as others see it and outline our refinement ...
  • Tree-based Density Estimation: Algorithms and Applications

    Schmidberger, Gabi (The University of Waikato, 2009)
    Data Mining can be seen as an extension to statistics. It comprises the preparation of data and the process of gathering new knowledge from it. The extraction of new knowledge is supported by various machine learning ...