Cantitate/Preț
Produs

Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach: Lecture Notes in Computer Science, cartea 10101

Editat de Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi
en Limba Engleză Paperback – 6 dec 2016
A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.
This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. 
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 33569 lei

Preț vechi: 41961 lei
-20% Nou

Puncte Express: 504

Preț estimativ în valută:
6424 6666$ 5369£

Carte tipărită la comandă

Livrare economică 15-29 martie

Preluare comenzi: 021 569.72.76

Specificații

ISBN-13: 9783319501369
ISBN-10: 3319501364
Pagini: 349
Ilustrații: XII, 349 p. 73 illus.
Dimensiuni: 155 x 235 x 19 mm
Greutate: 0.51 kg
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence

Locul publicării:Cham, Switzerland

Cuprins

Introduction to Combinatorial Optimisation in Numberjack.- Data Mining and Constraints: An Overview.- New Approaches to Constraint Acquisition.- ModelSeeker: Extracting Global Constraint Models from Positive Examples.- Learning Constraint Satisfaction Problems: An ILP Perspective.- Learning Modulo Theories.- Algorithm Selection for Combinatorial Search Problems: A Survey.- Adapting Consistency in Constraint Solving.- Modeling in MiningZinc.- Partition-Based Clustering Using Constraint Optimisation.- The Inductive Constraint Programming Loop.- ICON Loop Carpooling Show Case.- ICON Loop Health Show Case.- ICON Loop Energy Show Case.

Caracteristici

Reports on key results obtained in the field of data mining and constraint programming Integrated and cross-disciplinary approach Features state-of-the art research Includes supplementary material: sn.pub/extras