Measuring distance from a training data set. J, J., I., &., R. In pages 577-586, 2005. Proc. XI International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France. abstract bibtex In this paper, a new method is proposed for measuring the distance between a training data set and a single, new observation. The novel distance measure reflects the expected squared prediction error, when the prediction is based on the k nearest neighbours of the training data set. The simulation shows that the distance measure correlates well with the true expected squared prediction error in practice. The distance measure can be applied, for example, to assessing the uncertainty of prediction.
@inProceedings{
title = {Measuring distance from a training data set.},
type = {inProceedings},
year = {2005},
pages = {577-586},
publisher = {Proc. XI International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France},
id = {e70090d0-007c-38c7-8317-019c0d1f9e19},
created = {2019-11-19T13:00:38.966Z},
file_attached = {false},
profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},
group_id = {17585b85-df99-3a34-98c2-c73e593397d7},
last_modified = {2019-11-19T13:46:23.550Z},
read = {false},
starred = {false},
authored = {false},
confirmed = {true},
hidden = {false},
citation_key = {isg:591},
source_type = {inproceedings},
private_publication = {false},
abstract = {In this paper, a new method is proposed for measuring the distance between a training data set and a single, new observation. The novel distance measure reflects the expected squared prediction error, when the prediction is based on the k nearest neighbours of the training data set. The simulation shows that the distance measure correlates well with the true expected squared prediction error in practice. The distance measure can be applied, for example, to assessing the uncertainty of prediction.},
bibtype = {inProceedings},
author = {J, Juutilainen I & Röning}
}
Downloads: 0
{"_id":"Nbt5p7kth3pPgxGew","bibbaseid":"j-measuringdistancefromatrainingdataset-2005","authorIDs":[],"author_short":["J, J., I., &., R."],"bibdata":{"title":"Measuring distance from a training data set.","type":"inProceedings","year":"2005","pages":"577-586","publisher":"Proc. XI International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France","id":"e70090d0-007c-38c7-8317-019c0d1f9e19","created":"2019-11-19T13:00:38.966Z","file_attached":false,"profile_id":"bddcf02d-403b-3b06-9def-6d15cc293e20","group_id":"17585b85-df99-3a34-98c2-c73e593397d7","last_modified":"2019-11-19T13:46:23.550Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"citation_key":"isg:591","source_type":"inproceedings","private_publication":false,"abstract":"In this paper, a new method is proposed for measuring the distance between a training data set and a single, new observation. The novel distance measure reflects the expected squared prediction error, when the prediction is based on the k nearest neighbours of the training data set. The simulation shows that the distance measure correlates well with the true expected squared prediction error in practice. The distance measure can be applied, for example, to assessing the uncertainty of prediction.","bibtype":"inProceedings","author":"J, Juutilainen I & Röning","bibtex":"@inProceedings{\n title = {Measuring distance from a training data set.},\n type = {inProceedings},\n year = {2005},\n pages = {577-586},\n publisher = {Proc. XI International Symposium on Applied Stochastic Models and Data Analysis (ASMDA 2005), Brest, France},\n id = {e70090d0-007c-38c7-8317-019c0d1f9e19},\n created = {2019-11-19T13:00:38.966Z},\n file_attached = {false},\n profile_id = {bddcf02d-403b-3b06-9def-6d15cc293e20},\n group_id = {17585b85-df99-3a34-98c2-c73e593397d7},\n last_modified = {2019-11-19T13:46:23.550Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n citation_key = {isg:591},\n source_type = {inproceedings},\n private_publication = {false},\n abstract = {In this paper, a new method is proposed for measuring the distance between a training data set and a single, new observation. The novel distance measure reflects the expected squared prediction error, when the prediction is based on the k nearest neighbours of the training data set. The simulation shows that the distance measure correlates well with the true expected squared prediction error in practice. The distance measure can be applied, for example, to assessing the uncertainty of prediction.},\n bibtype = {inProceedings},\n author = {J, Juutilainen I & Röning}\n}","author_short":["J, J., I., &., R."],"bibbaseid":"j-measuringdistancefromatrainingdataset-2005","role":"author","urls":{},"downloads":0},"bibtype":"inProceedings","creationDate":"2019-11-19T13:17:06.018Z","downloads":0,"keywords":[],"search_terms":["measuring","distance","training","data","set","j"],"title":"Measuring distance from a training data set.","year":2005}