Learning from Data Streams in Evolving Environments Methods and Applications /

• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Sayed-Mouchaweh, Moamar (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Έκδοση: 1st ed. 2019.
• Σειρά: Studies in Big Data, 41
• Θέματα: Electrical engineering.; Quality control.; Reliability.; Industrial safety.; Data mining.; Control engineering.; Communications Engineering, Networks.; Quality Control, Reliability, Safety and Risk.; Data Mining and Knowledge Discovery.; Control and Systems Theory.

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 interpr...

Πίνακας περιεχομένων:

• 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.