Corpus ID: 2949558

On the effect of data set size on bias and variance in classification learning

  title={On the effect of data set size on bias and variance in classification learning},
  author={D. Brain and Geoffrey I. Webb},
  • D. Brain, Geoffrey I. Webb
  • Published 1999
  • Computer Science
  • With the advent of data mining, machine learning has come of age and is now a critical technology in many businesses. [...] Key Result These results have profound implications for data mining from large data sets, indicating that developing effective learning algorithms for large data sets is not simply a matter of finding computationally efficient variants of existing learning algorithms.Expand Abstract
    Making Early Predictions of the Accuracy of Machine Learning Applications
    • 2
    • PDF
    Learning with few examples: An empirical study on leading classifiers
    • 23
    • PDF
    Tree Induction Vs Logistic Regression: A Learning Curve Analysis
    • 308
    • PDF
    Concept-drifting Data Streams are Time Series; The Case for Continuous Adaptation
    • 3
    • PDF
    DO NOT DISTURB? Classifier Behavior on Perturbed Datasets
    • 10
    • PDF
    Scalable Learning of Bayesian Network Classifiers
    • 36
    • PDF
    Preconditioning an Artificial Neural Network Using Naive Bayes
    • 5
    • PDF


    Publications referenced by this paper.
    Bias, Variance , And Arcing Classifiers
    • 527
    • PDF
    Experiments with a New Boosting Algorithm
    • 7,715
    • PDF
    C4.5: Programs for Machine Learning
    • 20,153
    Boosting the margin: A new explanation for the effectiveness of voting methods
    • 2,555
    • PDF
    Programs for Machine Learning
    • 5,419
    Peepholing: Choosing Attributes Efficiently for Megainduction
    • 28
    Bagging predictors
    • 11,931
    • PDF
    An Analysis of Bayesian Classifiers
    • 1,296
    Error-Correcting Output Coding Corrects Bias and Variance
    • 412
    Bias Plus Variance Decomposition for Zero-One Loss Functions
    • 647
    • PDF