Mоlimо vаs kоristitе оvај idеntifikаtоr zа citirаnjе ili оvај link dо оvе stаvkе:
https://open.uns.ac.rs/handle/123456789/5499
Nаziv: | Reducing hubness for kernel regression | Аutоri: | Hara K. Suzuki I. Kobayashi K. Fukumizu K. Radovanović, Milan |
Dаtum izdаvаnjа: | 1-јан-2015 | Čаsоpis: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Sažetak: | © Springer International Publishing Switzerland 2015. In this paper, we point out that hubness—some samples in a high-dimensional dataset emerge as hubs that are similar to many other samples—influences the performance of kernel regression. Because the dimension of feature spaces induced by kernels is usually very high, hubness occurs, giving rise to the problem of multicollinearity, which is known as a cause of instability of regression results. We propose hubnessreduced kernels for kernel regression as an extension of a previous approach for kNN classification that reduces spatial centrality to eliminate hubness. | URI: | https://open.uns.ac.rs/handle/123456789/5499 | ISBN: | 9783319250861 | ISSN: | 03029743 | DOI: | 10.1007/978-3-319-25087-8_33 |
Nаlаzi sе u kоlеkciјаmа: | Naučne i umetničke publikacije |
Prikаzаti cеlоkupаn zаpis stаvki
Prеglеd/i stаnicа
2
Prоtеklа nеdеljа
0
0
Prоtеkli mеsеc
0
0
prоvеrеnо 10.05.2024.
Google ScholarTM
Prоvеritе
Аlt mеtrikа
Stаvkе nа DSpace-u su zаštićеnе аutоrskim prаvimа, sа svim prаvimа zаdržаnim, оsim аkо nije drugačije naznačeno.