Vector quantisation and its associated learning algorithms form an essential framework within modern machine learning, providing interpretable and computationally efficient methods for data ...

Abstract: Discriminative vector quantization schemes such as learning vector quantization (LVQ) and extensions thereof offer efficient and intuitive classifiers based on the representation of classes ...

Abstract: A new Learning Vector Quantization (LVQ) approach, so-called Dual Weight Learning Vector Quantization (DWLVQ), is presented in this paper. The basic idea is to introduce an additional weight ...

This paper discusses three basic blocks for the inference of convolutional neural networks (CNNs). Pyramid Vector Quantization [1] (PVQ) is discussed as an effective quantizer for CNNs weights ...

2014_IEEE Multimedia_PRVQ_Projected residual vector quantization for ANN search. [IEEE] 2015_arXiv_IRVQ_Improved Residual Vector Quantization for High-dimensional Approximate Nearest Neighbor Search. ...

ABSTRACT: β-turn is one of the most important reverse turns because of its role in protein folding. Many computational methods have been studied for predicting β-turns and β-turn types. However, due ...

A classification problem is a supervised learning problem that asks for a choice between two or more classes, usually providing probabilities for each class. Leaving out neural networks and deep ...