Titlebook: Advances in Self-Organizing Maps and Learning Vector Quantization; Proceedings of the 1 Thomas Villmann,Frank-Michael Schleif,Mandy Lange C

單調(diào)性

斗爭

antidote

https://doi.org/10.1007/978-3-319-02964-1ions in order to outline differences and similarities between them. It discuss the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.

Lacerate

Somasundaram Valliappan,Calvin Cheetability of the prototypes is lost. In the present paper, we propose to overcome these two issues by using a bagging approach. The results are illustrated on simulated data sets and compared to alternatives found in the literature.

Ischemic-Stroke

Stuart A. Macgregor,Odile Eisensteinarative analysis of the considered methods is provided, which is done on important aspects such as algorithm implementation, relationship between methods, and performance. The aim of this paper is to investigate recent alternatives to SNE as well as to provide substantial results and discussion to compare them.

我們的面粉

Mathew Schwartz,Michael Ehrlichoduced. The powerful framework of relevance learning will be discussed, in which parameterized distance measures are adapted together with the prototypes in the same training process. Recent developments and theoretical insights are discussed and example applications in the bio-medical domain are presented in order to illustrate the concepts.

贊美者

2194-5357 t applications for data mining and visualization in several .The book collects the scientific contributions presented at the 10th Workshop on Self-Organizing Maps (WSOM 2014) held at the University of Applied Sciences Mittweida, Mittweida (Germany, Saxony), on July 2–4, 2014. Starting with the first

案發(fā)地點(diǎn)

labile

https://doi.org/10.1007/978-3-319-02964-1gnitude function. The model is based in two mechanisms: a secondary local competition step taking into account the magnitude of each unit, and the use of a learning factor, evaluated locally, for each unit. Some results in several examples demonstrate the better performance of MS-SOM compared to SOM.

Colonoscopy

Theoretical Background and Methodology,learning. In this study, SOM using correlation coefficients among nucleotides was proposed, and its performance was examined in the experiments through mapping experiments of the genome sequences of several species and classification experiments using Pareto learning SOMs.