Daniel Nicolas Splitthoff

Artificial Intelligence · 2011

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 Daniel Nicolas Splitthoff trusts overall

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

  1. Zhuowen Tu
  2. Thomas G. Dietterich
  3. Leo Breiman
  4. Eric R. Ziegel
  5. Tin Kam Ho
  6. J. Platt
  7. Mark R. Segal
  8. Trevor J. Hastie
  9. Yoav Freund
  10. Andy Liaw

Papers from Daniel Nicolas Splitthoff's perspective

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

  1. Bagging Predictors
  2. Random Forests
  3. A decision-theoretic generalization of on-line learning and an application to boosting
  4. The Elements of Statistical Learning
  5. An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization
  6. Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods
  7. The Random Subspace Method for Constructing Decision Forests
  8. Extremely randomized trees
  9. Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation
  10. Consistency of Random Forests and Other Averaging Classifiers

Their papers