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Learning from Data Streams in Dynamic Environments

This book addresses the problems of modeling, prediction, classification, data understanding and processing in non-stationary and;

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Learning from Data Streams in Evolving Environments

This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by;

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Knowledge Discovery from Data Streams

coherent overview ofstate-of-the-art research in learning from data streams.;

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IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning

This book constitutes selected papers from the Second International Workshop on IoT Streams for Data-Driven Predictive Maintenance, IoT;

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Transactional Machine Learning with Data Streams and AutoML

Understand how to apply auto machine learning to data streams and create transactional machine learning (TML) solutions that are;

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Learning from Data Streams

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive;

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Learning from Data Streams

Processing data streams has raised new research challenges over the last few years. This book provides the reader with a comprehensive;

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Dynamic Fuzzy Machine Learning

Machine learning is widely used for data analysis. Dynamic fuzzy data are one of the most difficult types of data to analyse in the field;

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Formative Assessment, Learning Data Analytics and Gamification

Formative Assessment, Learning Data Analytics and Gamification: An ICT Education discusses the challenges associated with assessing student;

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Data Analytics Approaches in Educational Games and Gamification Systems

Game-based learning environments and learning analytics are attracting increasing attention from researchers and educators, since they both;

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Data Analytics Approaches in Educational Games and Gamification Systems

Game-based learning environments and learning analytics are attracting increasing attention from researchers and educators, since they both;

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Anomaly Detection and Complex Event Processing Over IoT Data Streams

signals from the sensors are processed dynamically across the data pipeline and classified with modern machine learning approaches including the;

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Computational Intelligence in Pattern Recognition

in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the;

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Foundations of Computational Intelligence 06

methodologies of data mining were derived from statistics, machine learning, and artificial intelligence, and are being used successfully in application;

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Oracle Streams 11G Data Replication

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to;

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Sentiment Analysis and Knowledge Discovery in Contemporary Business

source that discusses applications of sentiment analysis as well as data mining, machine learning algorithms, and big data streams in business;

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Real-time Linked Dataspaces

specialization in the processing of dynamic real-time data. Part II Data Support Services explores the design and evaluation of critical services;

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Real-time Linked Dataspaces

specialization in the processing of dynamic real-time data. Part II Data Support Services explores the design and evaluation of critical services;

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Web and Big Data

papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing;

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Web and Big Data

papers were carefully reviewed and selected from 240 submissions. The papers are organized around the following topics: spatial data processing;

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Big Data Analytics

This book constitutes the refereed proceedings of the 6th International Conference on Big Data analytics, BDA 2018, held in Warangal, India;

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Reactive Streams in Java

Get an easy introduction to reactive streams in Java to handle concurrency, data streams, and the propagation of change in today's;

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Event Streams in Action

processing data from multiple sources. It then guides the reader through examples using the unified log technologies Apache Kafka and Amazon;

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Big Data Analytics and Knowledge Discovery

from 61 submissions. The papers are organized in the following topical sections: Applications; patterns; RDF and streams; big data systems;

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Machine Learning and Knowledge Discovery in Databases

; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix;

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Machine Learning and Knowledge Discovery in Databases

; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix;

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Machine Learning and Knowledge Discovery in Databases

learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data;

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