Pavel Kordík

Artificial Intelligence · with Borovicka · 2012

A researcher in the Science corpus: 1 paper; trusts 7 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom Pavel Kordík trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Vladimir Naumovich Vapni
  2. Pedro M. Domingos
  3. David Hilton Wolpert
  4. Gary M. Weiss
  5. Larry Michael Manevitz

Papers from Pavel Kordík's perspective

Ordered by how strongly the researchers Pavel Kordík trusts endorse each paper. Same corpus, a different order for every researcher.

  1. MetaCost: a general method for making classifiers cost-sensitive
  2. The Nature of Statistical Learning Theory
  3. SMOTE: Synthetic Minority Over-sampling Technique
  4. A study of the behavior of several methods for balancing machine learning training data
  5. Learning When Training Data are Costly: The Effect of Class Distribution on Tree Induction (Journal of Artificial Intelligence Research)
  6. One-Class SVMs for Document Classification (Journal of machine learning research)
  7. Original Contribution: Stacked generalization
  8. The Foundations of Cost-Sensitive Learning
  9. Minority report in fraud detection: classification of skewed data (SKDD)
  10. Extreme re-balancing for SVMs: a case study

Their papers