Name: Multi-Layer Perceptron Networks for Ordinal Data Analysis -- Order Independent Online Learning by Sequential Estimation (Advances in Information Systems and Management Science) (en Inglés)
SKU: 62070110
Price: 96730.00 CLP
Availability: InStock
Author: Stephan Dlugosz
ISBN: 9783832519841

portada Multi-Layer Perceptron Networks for Ordinal Data Analysis -- Order Independent Online Learning by Sequential Estimation (Advances in Information Systems and Management Science) (en Inglés)

Multi-Layer Perceptron Networks for Ordinal Data Analysis -- Order Independent Online Learning by Sequential Estimation (Advances in Information Systems and Management Science) (en Inglés)

Formato

Libro Físico

Editorial

Logos Verlag

Autor

Stephan Dlugosz

Idioma

Inglés

N° páginas

280

Encuadernación

Tapa Blanda

ISBN13

9783832519841

Multi-Layer Perceptron Networks for Ordinal Data Analysis -- Order Independent Online Learning by Sequential Estimation (Advances in Information Systems and Management Science) (en Inglés)

Stephan Dlugosz (Autor) · Logos Verlag · Tapa Blanda

Libro Nuevo Importado

Envío: 16 a 22 días háb.

$ 175.880$ 96.730

-45%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Última Unidad

$ 96.730

Llega entre el 07 Oct y el 16 Oct a Santiago, Región Metropolitana. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Multi-Layer Perceptron Networks for Ordinal Data Analysis -- Order Independent Online Learning by Sequential Estimation (Advances in Information Systems and Management Science) (en Inglés)"

Whether the results of statistical procedures are accepted or not is strongly influenced by the way how they are interpreted. Effects based on data encodings and the order in which the data occurs in the learning sample are particularly problematic. Modern data collections often contain large numbers of items, each including many variables. These variables are usually measured on different scales among which the ordinal scale is the most common. Versatile and efficient data analysis models are required for mining these data. Multi-layer Perceptron (MLP) Networks are very flexible models for analyzing problems that have an input-output structure. These techniques are well-known in artificial intelligence and provide models for non-linear statistical regression and classification with efficient learning algorithms. The author of this thesis develops extensions to MLP networks suitable for the appropriate analysis of ordinal data occurring both as inputs and outputs. Reviewing the learning procedure he introduces a new learning paradigm that combines the advantages of batch learning on the one hand and incremental estimation on the other, i.e. statistically better results and algorithmic efficiency respectively. This allows an efficient online adaptation of the model without being compromised by the dependence on either a learning parameter or the ordering of the data set. This book addresses researchers, lecturers and students of mathematics, informatics and artificial intelligence. It may also be interesting for those who deal with data analysis in their daily work.