Results 21 to 30 of about 61,624 (324)
Analysis of Robust Soft Learning Vector Quantization and an application to Facial Expression Recognition [PDF]
, 2009 Learning Vector Quantization (LVQ) is a popular method for multiclass classification. Several variants of LVQ have been developed recently, of which Robust Soft Learning Vector Quantization (RSLVQ) is a promising one.
Biehl, Michael, de Vries, Gert-Jan
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ONLINE KERNEL AMGLVQ FOR ARRHYTHMIA HEARBEATS CLASSIFICATION
Jurnal Ilmiah Kursor: Menuju Solusi Teknologi Informasi, 2016 This study proposes Online Kernel Adaptive Multilayer Generalized Learning Vector Quantization (KAMGLVQ) for handling imbalanced data sets. KAMGLVQ is extended version of AMGLVQ that used kernel function to handling non-linear classification problems ...
Elly Matul Imah, R. Sulaiman
doaj +1 more source
Magnification Control in Self-Organizing Maps and Neural Gas [PDF]
, 2006 We consider different ways to control the magnification in self-organizing maps (SOM) and neural gas (NG). Starting from early approaches of magnification control in vector quantization, we then concentrate on different approaches for SOM and NG. We show
Brause R. +4 more
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Differential privacy for learning vector quantization [PDF]
Neurocomputing, 2019 Abstract Prototype-based machine learning methods such as learning vector quantisation (LVQ) offer flexible classification tools, which represent a classification in terms of typical prototypes. This representation leads to a particularly intuitive classification scheme, since prototypes can be inspected by a human partner in the same way as data ...
Brinkrolf, Johannes +2 more
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PREDIKSI TERJANGKITNYA PENYAKIT JANTUNG DENGAN METODE LEARNING VECTOR QUANTIZATION
Media Statistika, 2010 Learning Vector Quantization (LVQ) is a method that train the competitives layer with supervised. The competitives layer will learn automatically to classify the input vector given.
Nurul Hidayati, Budi Warsito
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Adaptive Relevance Matrices in Learning Vector Quantization [PDF]
Neural Computation, 2009 We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototype-based classification algorithm, toward a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme ...
Schneider, P. +2 more
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Introduction to vector quantization and its applications for numerics*
ESAIM: Proceedings and Surveys, 2015 We present an introductory survey to optimal vector quantization and its first applications to Numerical Probability and, to a lesser extent to Information Theory and Data Mining. Both theoretical results on the quantization rate of a
Pagès Gilles
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Learning from low precision samples
Proceedings of the International Florida Artificial Intelligence Research Society Conference, 2021 With advances in edge applications in industry and healthcare, machine learning models are increasingly trained on the edge. However, storage and memory infrastructure at the edge are often primitive, due to cost and real-estate constraints.
Ji In Choi +5 more
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Broad Absorption Line Quasar catalogues with Supervised Neural Networks [PDF]
, 2008 We have applied a Learning Vector Quantization (LVQ) algorithm to SDSS DR5 quasar spectra in order to create a large catalogue of broad absorption line quasars (BALQSOs).
Christian Knigge +4 more
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Identifikasi Barcode pada Gambar yang Ditangkap Kamera Digital Menggunakan Metode JST
IJCCS (Indonesian Journal of Computing and Cybernetics Systems), 2013 Abstrak Dewasa ini hampir setiap produk konsumen memiliki label barcode. Namun alat pembaca barcode jenis laser memiliki kelemahan karena tidak dapat mengenali barcode yang mengalami goresan atau noise.
Salman Aliaji, Agus Harjoko
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