B. de Ville

Statistical Learning Theory · 2013

A researcher in the Science corpus: 1 paper; trusts 8 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom B. de Ville trusts overall

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

  1. Robert E. Schapire
  2. Jerome H. Friedman
  3. Leo Breiman
  4. Yali Amit
  5. Yoav Freund
  6. J. Ross Quinlan
  7. William W. Cohen
  8. Niko Speybroeck
  9. Wei-Yin Loh
  10. Steven L. Salzberg

Papers from B. de Ville's perspective

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

  1. The Strength of Weak Learnability
  2. Stochastic gradient boosting
  3. Shape Quantization and Recognition with Randomized Trees
  4. The Nature of Statistical Learning Theory
  5. A decision-theoretic generalization of on-line learning and an application to boosting
  6. Bootstrap Methods: Another Look at the Jackknife
  7. Regression Shrinkage and Selection via the Lasso
  8. ImageNet classification with deep convolutional neural networks
  9. Problems in the Analysis of Survey Data, and a Proposal
  10. Bias, Variance , And Arcing Classifiers

Their papers