Self-Evolvable Systems Machine Learning in Social Media /

This monograph presents key method to successfully manage the growing  complexity of systems  where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evol...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Iordache, Octavian (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012.
Σειρά:Understanding Complex Systems,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
LEADER 02489nam a22004815i 4500
001 978-3-642-28882-1
003 DE-He213
005 20151116133259.0
007 cr nn 008mamaa
008 120704s2012 gw | s |||| 0|eng d
020 |a 9783642288821  |9 978-3-642-28882-1 
024 7 |a 10.1007/978-3-642-28882-1  |2 doi 
040 |d GrThAP 
050 4 |a QA76.9.M35 
072 7 |a GPFC  |2 bicssc 
072 7 |a TEC000000  |2 bisacsh 
082 0 4 |a 620  |2 23 
100 1 |a Iordache, Octavian.  |e author. 
245 1 0 |a Self-Evolvable Systems  |h [electronic resource] :  |b Machine Learning in Social Media /  |c by Octavian Iordache. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg :  |b Imprint: Springer,  |c 2012. 
300 |a XXII, 278 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
347 |a text file  |b PDF  |2 rda 
490 1 |a Understanding Complex Systems,  |x 1860-0832 
505 0 |a Introduction -- General Framework -- Differential Models -- Informational Criteria -- Self-Evolvability for Physical and Chemical Systems -- Self-Evolvability for Biosystems -- Self-Evolvability for Cognitive Systems -- Control Systems -- Manufacturing Systems -- Concept Lattices -- Design of Experiments -- Perspectives. 
520 |a This monograph presents key method to successfully manage the growing  complexity of systems  where conventional engineering and scientific methodologies and technologies based on learning and adaptability come to their limits and new ways are nowadays required. The transition from adaptable to evolvable and finally to self-evolvable systems is highlighted, self-properties such as self-organization, self-configuration, and self-repairing are introduced and challenges and limitations of the self-evolvable engineering systems are evaluated. 
650 0 |a Engineering. 
650 0 |a Statistical physics. 
650 0 |a Computational intelligence. 
650 0 |a Complexity, Computational. 
650 1 4 |a Engineering. 
650 2 4 |a Complexity. 
650 2 4 |a Computational Intelligence. 
650 2 4 |a Nonlinear Dynamics. 
710 2 |a SpringerLink (Online service) 
773 0 |t Springer eBooks 
776 0 8 |i Printed edition:  |z 9783642288814 
830 0 |a Understanding Complex Systems,  |x 1860-0832 
856 4 0 |u http://dx.doi.org/10.1007/978-3-642-28882-1  |z Full Text via HEAL-Link 
912 |a ZDB-2-PHA 
950 |a Physics and Astronomy (Springer-11651)