Learning Vector Quantization

By Romando Sipayung, Suamanda Ika Novichasari. 2010

315 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges (2021, background): “One of the earliest prototype learning techniques is learning vector quantization (LVQ) [159].”
Memory Efficient Experience Replay for Streaming Learning (2018, methodology): “Partitioning algorithms separate points into j disjoint clusters by minimizing an objective function [2], [20], [28], [43].”
Machine Learning in Agriculture: A Review (2018, methodology): “The most common learning algorithms in this category are the k-nearest neighbor [39], locally weighted learning [40], and learning vector quantization [41].”
Meta-learning for evolutionary parameter optimization of classifiers (2012, methodology): “(2001) presented G-LVQ, which uses a genetic algorithm to optimize Learning Vector Quantization (Kohonen 1998).”
Prototype-based Neural Network Layers: Incorporating Vector Quantization (2018, background): “K OHONEN in 1988 [31] and later extended [21].”
Moving Object Detection in Spatial Domain using Background Removal Techniques - State-of-Art (2008, methodology): “They adopt a quantization/clustering technique, inspired by Kohonen [63, 64].”
Statistical Mechanics of On-line Learning (2009, background): “The solid line marks the result for LVQ1 in the limits η → 0 and ηα → ∞, the dashed line corresponds to an idealized early stopping procedure applied to LVQ+/-, and the chain line represents the α → ∞ asymptotic outcome of LFM training.”