Kevin Lacker

Statistical Learning Theory · 2003

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

• Statistical Learning Theory — 5 citations into it

Whom Kevin Lacker trusts overall

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

1. Eric R. Ziegel
2. Bernhard Schölkopf
3. Alex Smola
4. Bernhard E. Boser
5. Robert Tibshirani
6. Federico Girosi

Who trusts Kevin Lacker overall

1. Yangqing Jia
2. Stephen H. Bach
3. Isabelle M Guyon
4. Holger Hofling
5. Mark W. Schmidt
6. Jun Liu
7. Su-In Lee
8. Shay B. Cohen
9. Thomas Lavergne
10. Guo-Xun Yuan

Papers from Kevin Lacker's perspective

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

1. Regression Shrinkage and Selection via the Lasso
2. The Elements of Statistical Learning
3. A training algorithm for optimal margin classifiers
4. ImageNet classification with deep convolutional neural networks
5. Learning with Kernels: support vector machines, regularization, optimization, and beyond
6. UCI Repository of machine learning databases
7. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
8. Ridge Regression: Biased Estimation for Nonorthogonal Problems
9. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
10. Gradient-based learning applied to document recognition

Their papers

• Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space (2003) — 384 citations