Êîíòàêòû/Ïðîåçä  Äîñòàâêà è Îïëàòà Ïîìîùü/Âîçâðàò
Èñòîðèÿ
  +7(495) 980-12-10
  ïí-ïò: 10-18 ñá,âñ: 11-18
  shop@logobook.ru
   
    Ïîèñê êíèã                    Ïîèñê ïî ñïèñêó ISBN Ðàñøèðåííûé ïîèñê    
Íàéòè
  Çàðóáåæíûå èçäàòåëüñòâà Ðîññèéñêèå èçäàòåëüñòâà  
Àâòîðû | Êàòàëîã êíèã | Èçäàòåëüñòâà | Íîâèíêè | Ó÷åáíàÿ ëèòåðàòóðà | Àêöèè | Õèòû | |
 

Realtime Data Mining, Alexander Paprotny; Michael Thess


Âàðèàíòû ïðèîáðåòåíèÿ
Öåíà: 13974.00ð.
Êîë-âî:
Íàëè÷èå: Ïîñòàâêà ïîä çàêàç.  Åñòü â íàëè÷èè íà ñêëàäå ïîñòàâùèêà.
Ñêëàä Àìåðèêà: Åñòü  
Ïðè îôîðìëåíèè çàêàçà äî: 2025-07-28
Îðèåíòèðîâî÷íàÿ äàòà ïîñòàâêè: Àâãóñò-íà÷àëî Ñåíòÿáðÿ
Ïðè óñëîâèè íàëè÷èÿ êíèãè ó ïîñòàâùèêà.

Äîáàâèòü â êîðçèíó
â Ìîè æåëàíèÿ

Àâòîð: Alexander Paprotny; Michael Thess
Íàçâàíèå:  Realtime Data Mining
ISBN: 9783319013206
Èçäàòåëüñòâî: Springer
Êëàññèôèêàöèÿ:



ISBN-10: 3319013203
Îáëîæêà/Ôîðìàò: Hardcover
Ñòðàíèöû: 313
Âåñ: 0.65 êã.
Äàòà èçäàíèÿ: 16.12.2013
Ñåðèÿ: Applied and Numerical Harmonic Analysis
ßçûê: English
Èçäàíèå: 1st ed. 2013. corr.
Èëëþñòðàöèè: 28 tables, black and white; 88 illustrations, color; 12 illustrations, black and white; xxiii, 313 p. 100 illus., 88 illus. in color.
Ðàçìåð: 243 x 158 x 24
×èòàòåëüñêàÿ àóäèòîðèÿ: Professional & vocational
Îñíîâíàÿ òåìà: Mathematics
Ïîäçàãîëîâîê: Self-Learning Techniques for Recommendation Engines
Ññûëêà íà Èçäàòåëüñòâî: Link
Ðåéòèíã:
Ïîñòàâëÿåòñÿ èç: Ãåðìàíèè
Îïèñàíèå: � � � � Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods.


Realtime Data Mining

Àâòîð: Alexander Paprotny; Michael Thess
Íàçâàíèå: Realtime Data Mining
ISBN: 3319344455 ISBN-13(EAN): 9783319344454
Èçäàòåëüñòâî: Springer
Ðåéòèíã:
Öåíà: 11878.00 ð.
Íàëè÷èå íà ñêëàäå: Åñòü ó ïîñòàâùèêà Ïîñòàâêà ïîä çàêàç.

Îïèñàíèå: � � � � Describing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods.


ÎÎÎ "Ëîãîñôåðà " Òåë:+7(495) 980-12-10 www.logobook.ru
   Â Êîíòàêòå     Â Êîíòàêòå Ìåä  Ìîáèëüíàÿ âåðñèÿ