Tomás Borovicka

Artificial Intelligence · 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 Tomás Borovicka 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
  6. Nitesh V. Chawla
  7. Clifton Phua
  8. J. A. Olvera-López
  9. Xinjian Guo
  10. Foster J. Provost

Papers from Tomás Borovicka's perspective

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

  1. The Nature of Statistical Learning Theory
  2. MetaCost: a general method for making classifiers cost-sensitive
  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. Gradient-based learning applied to document recognition

Their papers