Learning from data streams in evolving environments methods and applications - Sayed-Mouchaweh, Moamar; SpringerLink (Online service). (Cham [Germany] Springer International Publishing, 2019)
Introduction -- Transfer Learning in Non-Stationary Environments -- A new combination of diversity techniques in ensemble classifiers for handling complex concept drift -- Analyzing and Clustering Pareto-Optimal Objects in Data Streams -- Error-bounded Approximation of Data Stream: Methods and Theories -- Ensemble Dynamics in Non-stationary Data Stream Classification -- Processing Evolving Social Networks for Change Detection based on Centrality Measures -- Large-scale Learning from Data Streams with Apache SAMOA -- Process Mining for Analyzing Customer Relationship Management Systems A Case Study -- Detecting Smooth Cluster Changes in Evolving Graph Sequences -- Efficient Estimation of Dynamic Density Functions with Applications in Data Streams -- Incremental Support Vector Machine (SVM) Learning: Review -- On Social Network-based Algorithms for Data Stream Clustering.
Electronic reproduction.
Keywords: Automatic control; Process mining; Electrical engineering; Industrial safety; Quality control; Reliability (Engineering).; Support vector machines; Transfer learning (Machine learning).; Process mining -- Case studies; Electronic books