Semanlink - k-nearest neighbors

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Wikipedia k-nearest neighbors

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6 Έγγραφα (Όλα τα έγγραφα)

k-Nearest Neighbor lookups on embeddings: kNN vs SVM

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2023-04-15 About

Uri Alon sur Twitter : "K-nearest neighbors language models (kNN-LMs; @ukhndlwl et al., ICLR'2020) improve the perplexity of standard LMs... Why?"

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2023-01-11 About

[2203.08913] Memorizing Transformers

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2022-05-07 About

A novel multi-label classification algorithm based on K-nearest neighbor and random walk - Zhen-Wu Wang, Si-Kai Wang, Ben-Ting Wan, William Wei Song, 2020

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2020-08-11 About

[1703.03129] Learning to Remember Rare Events

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2018-10-23 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About

Ιδιότητες

sl:creationDate : 2016-01-17
sl:creationTime : 2016-01-17T23:48:14Z
sl:describedBy : https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm
rdf:type : sl:Tag
skos:altLabel : KNN@en
skos:prefLabel : k-nearest neighbors@en