Name: Knowledge Transfer Between Computer Vision and Text Mining: Similarity-Based Learning Approaches (en Inglés)
Price: 320.36 PEN
Availability: InStock
Author: Ionescu, Radu Tudor ; Popescu, Marius
ISBN: 9783319303659

portada Knowledge Transfer Between Computer Vision and Text Mining: Similarity-Based Learning Approaches (en Inglés)

Knowledge Transfer Between Computer Vision and Text Mining: Similarity-Based Learning Approaches (en Inglés)

Formato

Libro Físico

Autor

Marius Popescu

Radu Tudor Ionescu

Editorial

Springer

Idioma

Inglés

N° páginas

250

Encuadernación

Tapa Dura

Dimensiones

24.2 x 15.5 x 2.0 cm

Peso

0.67 kg.

ISBN13

9783319303659

Categorías

Aplicaciones gráficas y multimedia
Minería de datos («data mining»)

Knowledge Transfer Between Computer Vision and Text Mining: Similarity-Based Learning Approaches (en Inglés)

Marius Popescu (Autor) · Radu Tudor Ionescu (Autor) · Springer · Tapa Dura

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Reseña del libro "Knowledge Transfer Between Computer Vision and Text Mining: Similarity-Based Learning Approaches (en Inglés)"

This ground-breaking text/reference diverges from the traditional view that computer vision (for image analysis) and string processing (for text mining) are separate and unrelated fields of study, propounding that images and text can be treated in a similar manner for the purposes of information retrieval, extraction and classification. Highlighting the benefits of knowledge transfer between the two disciplines, the text presents a range of novel similarity-based learning (SBL) techniques founded on this approach. Topics and features: describes a variety of SBL approaches, including nearest neighbor models, local learning, kernel methods, and clustering algorithms; presents a nearest neighbor model based on a novel dissimilarity for images; discusses a novel kernel for (visual) word histograms, as well as several kernels based on a pyramid representation; introduces an approach based on string kernels for native language identification; contains links for downloading relevant open source code.