|
" Learning from data streams in evolving environments : "
Moamar Sayed-Mouchaweh, editor.
| Document Type
|
:
|
BL
|
| Record Number
|
:
|
866044
|
| Title & Author
|
:
|
Learning from data streams in evolving environments : : methods and applications /\ Moamar Sayed-Mouchaweh, editor.
|
| Publication Statement
|
:
|
Cham, Switzerland :: Springer,, [2019]
|
| Series Statement
|
:
|
Studies in big data ;; volume 41
|
| Page. NO
|
:
|
1 online resource
|
| ISBN
|
:
|
3319898035
|
|
|
:
|
: 9783319898032
|
|
|
:
|
3319898027
|
|
|
:
|
9783319898025
|
| Contents
|
:
|
Chapter1: Transfer Learning in Non-Stationary Environments -- Chapter2: A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Chapter3: Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Chapter4: Error-bounded Approximation of Data Stream: Methods and Theories -- Chapter5: Ensemble Dynamics in Non-stationary Data Stream Classification -- Chapter6: Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Chapter7: Large-scale Learning from Data Streams with Apache SAMOA -- Chapter8: Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Chapter9: Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Chapter10: Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Chapter11: A Survey of Methods of Incremental Support Vector Machine Learning -- Chapter12: On Social Network-based Algorithms for Data Stream Clustering.
|
| Abstract
|
:
|
This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.
|
| Subject
|
:
|
Big data.
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Automatic control engineering.
|
| Subject
|
:
|
Big data.
|
| Subject
|
:
|
Communications engineering-- telecommunications.
|
| Subject
|
:
|
COMPUTERS-- General.
|
| Subject
|
:
|
Data mining.
|
| Subject
|
:
|
Machine learning.
|
| Subject
|
:
|
Reliability engineering.
|
| Dewey Classification
|
:
|
006.3/1
|
| LC Classification
|
:
|
Q325.5
|
| Added Entry
|
:
|
Sayed-Mouchaweh, Moamar
|
| |