A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and ...

This paper is concerned with a SOM-based data mining strategy for adaptive modelling of a slowly varying process. The aim is to follow the process in a way that ...

A SOM based model combination strategy, allowing to cre- ate adaptive – data dependent – committees, is proposed. Both, models included into a committee and ...

A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and ...

A SOM based model combination strategy, allowing to create adaptive – data dependent – committees, is proposed. Both, models included into a committee and ...

SOM consists of a training phase and a clustering phase. In the first stage, the training data is randomly selected, the winning neurons are selected according ...

The objective of this exploration is to propose a model that joins Self Organizing Map (SOM), Support Vector Reggression (SVR), and Local Mean-Based K-Nearest ...

Aug 25, 2020 · This paper proposes a clustering ensemble method that introduces cascade structure into the self-organizing map (SOM) to solve the problem ...

This work addresses a method based in Self-Organizing Maps ANN and K-Nearest Neighbor statistical classifier, called SOM-KNN, applied to digits recognition ...

This figure shows the algorithm used to develop SARIMA forecasting models. Processed target data is fed to the model where the model is trained, tested, and ...