Can learning vector quantization be an alternative to svm and deep learning?-Recent trends and advanced variants of learning vector quantization for classification …
T Villmann, A Bohnsack, M Kaden - Journal of Artificial Intelligence and …, 2017 - sciendo.com
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 …
Aspects in classification learning-Review of recent developments in Learning Vector Quantization
Classification is one of the most frequent tasks in machine learning. However, the variety of
classification tasks as well as classifier methods is huge. Thus the question is coming up …
classification tasks as well as classifier methods is huge. Thus the question is coming up …
Optimal local rejection for classifiers
We analyse optimal rejection strategies for classifiers with input space partitioning, eg
prototype-based classifiers, support vector machines or decision trees using certainty …
prototype-based classifiers, support vector machines or decision trees using certainty …
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 …
Parameter selection method for support vector regression based on adaptive fusion of the mixed kernel function
H Wang, D Xu - Journal of Control Science and Engineering, 2017 - Wiley Online Library
Support vector regression algorithm is widely used in fault diagnosis of rolling bearing. A
new model parameter selection method for support vector regression based on adaptive …
new model parameter selection method for support vector regression based on adaptive …
A momentum-incorporated latent factorization of tensors model for temporal-aware QoS missing data prediction
Abstract Quality-of-service (QoS) of Web services vary over time, making it a significant issue
to discover temporal patterns from them for addressing various subsequent analyzing tasks …
to discover temporal patterns from them for addressing various subsequent analyzing tasks …
Types of (dis-) similarities and adaptive mixtures thereof for improved classification learning
In this paper, we introduce taxonomies for similarity and dissimilarity measures, respectively,
based on their mathematical properties. Further, we propose a definition for rank …
based on their mathematical properties. Further, we propose a definition for rank …
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 …
Median variants of learning vector quantization for learning of dissimilarity data
Exemplar based techniques such as affinity propagation represent data in terms of typical
exemplars. This has two benefits:(i) the resulting models are directly interpretable by …
exemplars. This has two benefits:(i) the resulting models are directly interpretable by …