G. Handelman

Artificial Intelligence · 2018–2019

A researcher in the Science corpus: 2 papers; trusts 3 researchers and is trusted by 0 researchers here, as inferred from the citation graph.

Subfields

Whom G. Handelman trusts overall

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

  1. Paul Sajda
  2. Markus Ringnér
  3. Y. Fukuoka
  4. Ronald M. Summers
  5. Shijun Wang
  6. Isabelle M Guyon
  7. Joseph A. Cruz
  8. Erik Gawehn
  9. Michael Egmont-Petersen
  10. Leo Breiman

Papers from G. Handelman's perspective

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

  1. An Introduction to Variable and Feature Selection
  2. ImageNet classification with deep convolutional neural networks
  3. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition by Trevor Hastie, Robert Tibshirani, Jerome Friedman
  4. Computed tomographic virtual colonoscopy computer-aided polyp detection in a screening population. (Gastroenterology)
  5. Gradient-based learning applied to document recognition
  6. Applications of Machine Learning in Cancer Prediction and Prognosis (Cancer Informatics)
  7. The Nature of Statistical Learning Theory
  8. On Bias, Variance, 0/1—Loss, and the Curse-of-Dimensionality
  9. Computing Machinery and Intelligence
  10. Original Contribution: Stacked generalization

Their papers