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https://issues.apache.org/jira/browse/FLINK-1934?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Daniel Blazevski updated FLINK-1934:
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Description:
kNN is still a widely used algorithm for classification and regression.
However, due to the computational costs of an exact implementation, it does not
scale well to large amounts of data. Therefore, it is worthwhile to also add an
approximative kNN implementation as proposed in [1,2].
Resources:
[1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
[2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
[3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf
was:
kNN is still a widely used algorithm for classification and regression.
However, due to the computational costs of an exact implementation, it does not
scale well to large amounts of data. Therefore, it is worthwhile to also add an
approximative kNN implementation as proposed in [1,2].
Resources:
[1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
[2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
> Add approximative k-nearest-neighbours (kNN) algorithm to machine learning
> library
> ----------------------------------------------------------------------------------
>
> Key: FLINK-1934
> URL: https://issues.apache.org/jira/browse/FLINK-1934
> Project: Flink
> Issue Type: New Feature
> Components: Machine Learning Library
> Reporter: Till Rohrmann
> Assignee: Raghav Chalapathy
> Labels: ML
>
> kNN is still a widely used algorithm for classification and regression.
> However, due to the computational costs of an exact implementation, it does
> not scale well to large amounts of data. Therefore, it is worthwhile to also
> add an approximative kNN implementation as proposed in [1,2].
> Resources:
> [1] https://www.cs.utah.edu/~lifeifei/papers/mrknnj.pdf
> [2] http://www.computer.org/csdl/proceedings/wacv/2007/2794/00/27940028.pdf
> [3] http://cs.sjtu.edu.cn/~yaobin/papers/icde10_knn.pdf
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