|
| Titre : |
Linear algebra tools for data mining |
| Type de document : |
document électronique |
| Auteurs : |
Dan A. Simovici, Auteur |
| Mention d'édition : |
Second edition |
| Editeur : |
Singapore : World scientific |
| Année de publication : |
2012 |
| Importance : |
863 p. |
| Format : |
1 ressource en ligne |
| Accompagnement : |
CD |
| ISBN/ISSN/EAN : |
978-981-4383-49-3 |
| Note générale : |
Bibliogr. p. 843-851 |
| Langues : |
Anglais (eng) |
| Tags : |
Algèbre linéaire Exploration de données |
| Index. décimale : |
512.5 Algèbre linéaire |
| Résumé : |
This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.
Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).
The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications. The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases. |
| En ligne : |
https://doi.org/10.1142/13248 |
Linear algebra tools for data mining [document électronique] / Dan A. Simovici, Auteur . - Second edition . - Singapore : World scientific, 2012 . - 863 p. ; 1 ressource en ligne + CD. ISBN : 978-981-4383-49-3 Bibliogr. p. 843-851 Langues : Anglais ( eng)
| Tags : |
Algèbre linéaire Exploration de données |
| Index. décimale : |
512.5 Algèbre linéaire |
| Résumé : |
This updated compendium provides the linear algebra background necessary to understand and develop linear algebra applications in data mining and machine learning.
Basic knowledge and advanced new topics (spectral theory, singular values, decomposition techniques for matrices, tensors and multidimensional arrays) are presented together with several applications of linear algebra (k-means clustering, biplots, least square approximations, dimensionality reduction techniques, tensors and multidimensional arrays).
The useful reference text includes more than 600 exercises and supplements, many with completed solutions and MATLAB applications. The volume benefits professionals, academics, researchers and graduate students in the fields of pattern recognition/image analysis, AI, machine learning and databases. |
| En ligne : |
https://doi.org/10.1142/13248 |
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