The Testaments out now
Realtime Data Mining: Self-Learning Techniques for Recommendation Engines - Applied and Numerical Harmonic Analysis (Paperback)
  • Realtime Data Mining: Self-Learning Techniques for Recommendation Engines - Applied and Numerical Harmonic Analysis (Paperback)
zoom

Realtime Data Mining: Self-Learning Techniques for Recommendation Engines - Applied and Numerical Harmonic Analysis (Paperback)

(author), (author)
£76.50
Paperback 313 Pages / Published: 27/08/2016
  • We can order this

Usually dispatched within 3 weeks

  • This item has been added to your basket

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.

Publisher: Birkhauser
ISBN: 9783319344454
Number of pages: 313
Weight: 5153 g
Dimensions: 235 x 155 x 18 mm
Edition: Softcover reprint of the original 1st ed. 201

You may also be interested in...

Mathematical Techniques
Added to basket
Introducing Infinity
Added to basket
Foundation Mathematics for Biosciences
Added to basket
Linear Algebra
Added to basket
£31.49
Paperback
Maths for Chemists
Added to basket
Mathematical Physics
Added to basket
Calculations for A Level Physics
Added to basket
Molecular Symmetry and Group Theory
Added to basket
What Is Mathematics?
Added to basket
Complex Variables
Added to basket
£49.95
Paperback
Advanced Engineering Mathematics
Added to basket
Maths for Chemistry
Added to basket
£31.99
Paperback

Please sign in to write a review

Your review has been submitted successfully.