Learning Kernel Perceptrons on Noisy Data Using Random Projections

By Guillaume Stempfel, Liva Ralaivola. 2007

14 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Nagarajan Natarajan
2. Pradeep Ravikumar
3. Inderjit S. Dhillon
4. Ambuj Tewari
5. Peter Richtárik
6. Arindam Banerjee
7. Joydeep Ghosh
8. Pavel Berkhin

In Computer Vision and Pattern Recognition

1. Nagarajan Natarajan
2. Pradeep Ravikumar
3. Inderjit S. Dhillon
4. Ambuj Tewari

Citations in the corpus

• Ieee Transactions on Pattern Analysis and Machine Intelligence Classification with Noisy Labels by Importance Reweighting (unknown): “Perceptron algorithms that tolerate RCN have also been widely studied; see, for examples, [24]– [27].”
• Learning with Noisy Labels (2013, methodology): “We compare our methods with three state-of-the-art methods for dealing with random classification noise: Random Projection (RP) classifier [Stempfel and Ralaivola, 2007]), NHERD”