Incremental on-line learning: A review and comparison of state of the art algorithms
Recently, incremental and on-line learning gained more attention especially in the context of
big data and learning from data streams, conflicting with the traditional assumption of …
big data and learning from data streams, conflicting with the traditional assumption of …
Prototype‐based models in machine learning
An overview is given of prototype‐based models in machine learning. In this framework,
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …
observations, ie, data, are stored in terms of typical representatives. Together with a suitable …
Incremental learning algorithms and applications
Incremental learning refers to learning from streaming data, which arrive over time, with
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
limited memory resources and, ideally, without sacrificing model accuracy. This setting fits …
Deep adversarial metric learning
Learning an effective distance metric between image pairs plays an important role in visual
analysis, where the training procedure largely relies on hard negative samples. However …
analysis, where the training procedure largely relies on hard negative samples. However …
What you see is what you can change: Human-centered machine learning by interactive visualization
Visual analytics (VA) systems help data analysts solve complex problems interactively, by
integrating automated data analysis and mining, such as machine learning (ML) based …
integrating automated data analysis and mining, such as machine learning (ML) based …
[HTML][HTML] FDG-PET combined with learning vector quantization allows classification of neurodegenerative diseases and reveals the trajectory of idiopathic REM sleep …
R van Veen, SK Meles, RJ Renken, FE Reesink… - Computer Methods and …, 2022 - Elsevier
Background and Objectives: 18 F-fluorodeoxyglucose (FDG) positron emission tomography
(PET) combined with principal component analysis (PCA) has been applied to identify …
(PET) combined with principal component analysis (PCA) has been applied to identify …
[HTML][HTML] Subspace corrected relevance learning with application in neuroimaging
In machine learning, data often comes from different sources, but combining them can
introduce extraneous variation that affects both generalization and interpretability. For …
introduce extraneous variation that affects both generalization and interpretability. For …
Matrix relevance learning from spectral data for diagnosing cassava diseases
We discuss the use of matrix relevance learning, a popular extension to prototype learning
algorithms, applied to a three-class classification task of diagnosing cassava diseases from …
algorithms, applied to a three-class classification task of diagnosing cassava diseases from …
Can learning vector quantization be an alternative to svm and deep learning?-Recent trends and advanced variants of learning vector quantization for classification …
Learning vector quantization (LVQ) is one of the most powerful approaches for prototype
based classification of vector data, intuitively introduced by Kohonen. The prototype …
based classification of vector data, intuitively introduced by Kohonen. The prototype …
sklvq: Scikit learning vector quantization
The sklvq package is an open-source Python implementation of a set of learning vector
quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ …
quantization (LVQ) algorithms. In addition to providing the core functionality for the GLVQ …