The coming of age of interpretable and explainable machine learning models
Abstract Machine-learning-based systems are now part of a wide array of real-world
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
applications seamlessly embedded in the social realm. In the wake of this realization, strict …
Quantum-inspired learning vector quantizers for prototype-based classification: Confidential: for personal use only—submitted to Neural Networks and Applications 5 …
Prototype-based models like the Generalized Learning Vector Quantization (GLVQ) belong
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …
to the class of interpretable classifiers. Moreover, quantum-inspired methods get more and …
Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences
M Kaden, KS Bohnsack, M Weber, M Kudła… - Neural Computing and …, 2022 - Springer
We present an approach to discriminate SARS-CoV-2 virus types based on their RNA
sequence descriptions avoiding a sequence alignment. For that purpose, sequences are …
sequence descriptions avoiding a sequence alignment. For that purpose, sequences are …
Investigation of activation functions for generalized learning vector quantization
T Villmann, J Ravichandran, A Villmann… - Advances in Self …, 2020 - Springer
An appropriate choice of the activation function plays an important role for the performance
of (deep) multilayer perceptrons (MLP) in classification and regression learning. Usually …
of (deep) multilayer perceptrons (MLP) in classification and regression learning. Usually …
Variants of dropconnect in learning vector quantization networks for evaluation of classification stability
Dropout and DropConnect are useful methods to prevent multilayer neural networks from
overfitting. In addition, it turns out that these tools can also be used to estimate the stability of …
overfitting. In addition, it turns out that these tools can also be used to estimate the stability of …
Prototype-based neural network layers: incorporating vector quantization
Neural networks currently dominate the machine learning community and they do so for
good reasons. Their accuracy on complex tasks such as image classification is unrivaled at …
good reasons. Their accuracy on complex tasks such as image classification is unrivaled at …
A learning vector quantization architecture for transfer learning based classification in case of multiple sources by means of null-space evaluation
T Villmann, D Staps, J Ravichandran… - … on Intelligent Data …, 2022 - Springer
We present a method, which allows to train a Generalized Matrix Learning Vector
Quantization (GMLVQ) model for classification using data from several, maybe non …
Quantization (GMLVQ) model for classification using data from several, maybe non …
[PDF][PDF] Quantum-Inspired Learning Vector Quantization for Classification Learning.
This paper introduces a variant of the prototype-based generalized learning vector
quantization algorithm (GLVQ) for classification learning, which is inspired by quantum …
quantization algorithm (GLVQ) for classification learning, which is inspired by quantum …
Possibilistic classification learning based on contrastive loss in learning vector quantizer networks
Classification in a possibilistic scenario is a kind of multiple class assignments for data. One
of the most prominent and interpretable classifier is the learning vector quantization (LVQ) …
of the most prominent and interpretable classifier is the learning vector quantization (LVQ) …
Analysis of SARS-CoV-2 RNA-sequences by interpretable machine learning models
M Kaden, KS Bohnsack, M Weber, M Kudła… - BioRxiv, 2020 - biorxiv.org
We present an approach to investigate SARS-CoV-2 virus sequences based on alignment-
free methods for RNA sequence comparison. In particular, we verify a given clustering result …
free methods for RNA sequence comparison. In particular, we verify a given clustering result …