A class of logistic‐type discriminant functions

By Shinto Eguchi, J. Copas. 2002

88 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f4b7e33f1d2a818a3ccd6e68f79533c79b80ff9e

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. Shinto Eguchi
2. Takafumi Kanamori
3. Takashi Takenouchi
4. Noboru Murata
5. J. Copas
6. Balaji Lakshminarayanan

In Statistics and Probability

1. Shinto Eguchi
2. J. Copas
3. Takafumi Kanamori
4. Takashi Takenouchi
5. Noboru Murata
6. Paul R. Rosenbaum
7. Masashi Sugiyama
8. Dylan S. Small

Citations in the corpus

• Interpreting Kullback-Leibler divergence with the Neyman-Pearson lemma (2006, background): “[8] exploit this interpretation by minimizing this area over a parametric family of statistics λ̃(x).”
• Information Divergence Geometry and the Application to Statistical Machine Learning (2009, background): “Many other loss functions similar to the log-loss function including the area under the ROC curve was discussed in [12].”
• Robust Boosting Algorithm Against Mislabeling in Multiclass Problems (2008, background): “See [6] for a discussion of the relevant statistics.”