Consensus–relevance kNN and covariate shift mitigation

Author:

Kalpakis KonstantinosORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference56 articles.

1. Abramson, I. S. (1982). On bandwidth variation in kernel estimates-a square root law. Annals of Statistics, 10(4), 1217–1223. https://doi.org/10.1214/aos/1176345986

2. Anava, O. & Levy, K. Y. (2016). k*-nearest neighbors: From global to local, NIPS. arXiv:1701.07266.

3. Balsubramani, A., Dasgupta, S., Freund, Y. & Moran, S. (2019). An adaptive nearest neighbor rule for classification, NIPS. arXiv:1905.12717.

4. Bhapkar, V. P. (1966). A note on the equivalence of two test criteria for hypotheses in categorical data. Journal of the American Statistical Association, 61(313), 228–235.

5. Bickel, S., Brückner, M., & Scheffer, T. (2009). Discriminative learning under covariate shift. JMLR, 10, 2137–2155.

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