Semanlink - Machine learning: techniques ; Classification AND Similarity queries / Vector search

Machine learning: techniques ; Classification AND Similarity queries / Vector search

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5 Documents

k-Nearest Neighbor lookups on embeddings: kNN vs SVM

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2023-04-15 About

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 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

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

(fastText) Euclidean distance instead of cosine-similarity?

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2017-12-07 About