Prototype Classification: Insights from Machine Learning

By Arnulf B. A. Graf, Olivier Bousquet, Gunnar Rätsch, Bernhard Schölkopf. 2009

31 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

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

In Artificial Intelligence

In Computer Vision and Pattern Recognition

Citations in the corpus

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification (2014, methodology): “) BCM is intended as the third model type, but uses unsupervised generative mechanisms to explain clusters, rather than supervised approaches [16] or by focusing myopically on neighboring points [3].”
Prototypical Representation Learning for Relation Extraction (2021, background): “The idea of mean-ofclass prototypes could also be traced to earlier studies in machine learning (Graf et al., 2009) and cognitive modeling and psychology (Reed, 1972; Rosch et al., 1976).”
Interactive and interpretable machine learning models for human machine collaboration (2015, methodology): “) BCM is intended as the third model type, but uses unsupervised generative mechanisms to explain clusters, rather than supervised approaches [61] or by focusing myopically on neighboring points [13].”
Prototype-Incorporated Emotional Neural Network (2018, methodology): “In another related work, a two-stage-based generalized prototype framework was described; data dimensionality reduction was achieved in the first stage via projection onto a line, while a thresholding stage was used for the discrimination of projected data distribution [23], i.”
Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review (2017, unknown): “Such a classifier can be found in the literature under the name Minimum Distance to Mean (MDM), Nearest Centroid or Mean-of-Class Prototype [73].”