Realtime Data Mining (Record no. 96534)

000 -LEADER
fixed length control field 04176nam a22004935i 4500
001 - CONTROL NUMBER
control field 978-3-319-01321-3
003 - CONTROL NUMBER IDENTIFIER
control field DE-He213
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140220082840.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 131203s2013 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783319013213
-- 978-3-319-01321-3
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.1007/978-3-319-01321-3
Source of number or code doi
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA71-90
072 #7 - SUBJECT CATEGORY CODE
Subject category code PDE
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM014000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code MAT003000
Source bisacsh
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 004
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Paprotny, Alexander.
Relator term author.
245 10 - TITLE STATEMENT
Title Realtime Data Mining
Medium [electronic resource] :
Remainder of title Self-Learning Techniques for Recommendation Engines /
Statement of responsibility, etc by Alexander Paprotny, Michael Thess.
264 #1 -
-- Cham :
-- Springer International Publishing :
-- Imprint: Birkhäuser,
-- 2013.
300 ## - PHYSICAL DESCRIPTION
Extent XXIII, 313 p. 100 illus., 12 illus. in color.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- rda
490 1# - SERIES STATEMENT
Series statement Applied and Numerical Harmonic Analysis,
International Standard Serial Number 2296-5009
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1 Brave New Realtime World – Introduction -- 2 Strange Recommendations? – On The Weaknesses Of Current Recommendation Engines -- 3 Changing Not Just Analyzing – Control Theory And Reinforcement Learning -- 4 Recommendations As A Game – Reinforcement Learning For Recommendation Engines -- 5 How Engines Learn To Generate Recommendations – Adaptive Learning Algorithms -- 6 Up The Down Staircase – Hierarchical Reinforcement Learning -- 7 Breaking Dimensions – Adaptive Scoring With Sparse Grids -- 8 Decomposition In Transition - Adaptive Matrix Factorization -- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization -- 10 The Big Picture – Towards A Synthesis Of Rl And Adaptive Tensor Factorization -- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests -- 12 Building A Recommendation Engine – The Xelopes Library -- 13 Last Words – Conclusion -- References -- Summary Of Notation.
520 ## - SUMMARY, ETC.
Summary, etc 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. Furthermore, it presents promising results of numerous experiments on real-world data.  The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed.   This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer science.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computer software.
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Computational Science and Engineering.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical Applications in Computer Science.
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematical Software.
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Thess, Michael.
Relator term author.
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783319013206
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Applied and Numerical Harmonic Analysis,
-- 2296-5009
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-319-01321-3
912 ## -
-- ZDB-2-SMA

No items available.

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