Êîíòàêòû/Ïðîåçä  Äîñòàâêà è Îïëàòà Ïîìîùü/Âîçâðàò
Èñòîðèÿ
  +7 707 857-29-98
  +7(7172) 65-23-70
  10:00-18:00 ïí-ïò
  shop@logobook.kz
   
    Ïîèñê êíèã                        
Íàéòè
  Çàðóáåæíûå èçäàòåëüñòâà Ðîññèéñêèå èçäàòåëüñòâà  
Àâòîðû | Êàòàëîã êíèã | Èçäàòåëüñòâà | Íîâèíêè | Ó÷åáíàÿ ëèòåðàòóðà | Àêöèè | Áåñòñåëëåðû | |
 

Realtime Data Mining, Alexander Paprotny; Michael Thess


Âàðèàíòû ïðèîáðåòåíèÿ
Öåíà: 93160.00T
Êîë-âî:
Íàëè÷èå: Ïîñòàâêà ïîä çàêàç.  Åñòü â íàëè÷èè íà ñêëàäå ïîñòàâùèêà.
Ñêëàä Àìåðèêà: 206 øò.  
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: 79190.00 T
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.


Êàçàõñòàí, 010000 ã. Àñòàíà, ïðîñïåêò Òóðàí 43/5, ÍÏ2 (îôèñ 2)
ÒÎÎ "Ëîãîáóê" Òåë:+7 707 857-29-98 ,+7(7172) 65-23-70 www.logobook.kz
Kaspi QR
   Â Êîíòàêòå     Â Êîíòàêòå Ìåä  Ìîáèëüíàÿ âåðñèÿ