H. Alejandro Ceccatto

2005

A researcher in the Science corpus: 1 paper; trusts 10 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Whom H. Alejandro Ceccatto trusts overall

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

  1. Leo Breiman
  2. Jerome H. Friedman
  3. Yoav Freund

Papers from H. Alejandro Ceccatto's perspective

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

  1. Bagging Predictors
  2. A decision-theoretic generalization of on-line learning and an application to boosting
  3. Greedy function approximation: A gradient boosting machine.
  4. Random Forests
  5. Neural Networks and the Bias/Variance Dilemma
  6. Predicting Time Series with Support Vector Machines
  7. Ensembling neural networks: Many could be better than all
  8. ImageNet classification with deep convolutional neural networks
  9. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
  10. Gradient-based learning applied to document recognition

Their papers