Weighted k-Nearest-Neighbor Techniques and Ordinal Classification
By Klaus Hechenbichler, Klaus Schliep. 2004
373 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fbd45b85c720a79be7d4d3cb14b984bcf3901ebb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- Sadi Alawadi
- M. Febrero–Bande
- Senén Barro
- M. Delgado
- Eva Cernadas
- Maria Schuld
- Manisha Sanjay Sirsat
In Statistics and Probability
- Sadi Alawadi
- M. Febrero–Bande
- Senén Barro
- M. Delgado
- Eva Cernadas
- Maria Schuld
- Manisha Sanjay Sirsat
- Wenceslao González-Manteiga
Citations in the corpus
- An introduction to quantum machine learning (2014, background): “Another variation weights the influence of the neighbours by distance, gaining an independence of the parameter k (the weighted nearest neighbours algorithm [38]).”
- An extensive experimental survey of regression methods (2019, methodology): “kknn performs weighted k-nearest neighbors regression [38], imple917 mented by the kknn package.”