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Computer assisted instruction --- Teaching --- Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- Computer. Automation --- computervisie --- DIP (documentimage processing) --- beeldverwerking --- NLP (neurolinguïstisch programmeren) --- grafische vormgeving --- onderwijs --- computerondersteund onderwijs --- opvoeding --- programmeren (informatica) --- database management --- AI (artificiële intelligentie)
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This book constitutes the proceedings of the 21st International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Louvain-la-Neuve, Belgium, during April 12-14, 2023. The 38 papers included in this book were carefully reviewed and selected from 91 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale. .
Database management. --- Education—Data processing. --- Image processing—Digital techniques. --- Computer vision. --- Artificial intelligence. --- Machine learning. --- Natural language processing (Computer science). --- Database Management. --- Computers and Education. --- Computer Imaging, Vision, Pattern Recognition and Graphics. --- Artificial Intelligence. --- Machine Learning. --- Natural Language Processing (NLP). --- NLP (Computer science) --- Artificial intelligence --- Electronic data processing --- Human-computer interaction --- Semantic computing --- Learning, Machine --- Machine theory --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Machine vision --- Vision, Computer --- Image processing --- Pattern recognition systems --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Mathematical statistics --- Data processing
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"Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to the different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Several machine learning methods are inspected with respect to their resource requirements and how to enhance their scalability on diverse computing architectures ranging from embedded systems to large computing clusters. Ranges from embedded systems to large computing clusters. Provides application of the methods in various domains of science and engineering."--Provided by publisher.
SCIENCE / Chemistry / General. --- Artificial Intelligence. --- Big Data and Machine Learning. --- Cyber-physical systems. --- Data mining for Ubiquitous System Software. --- Embedded Systems and Machine Learning. --- Highly Distributed Data. --- ML on Small devices. --- Machine learning for knowledge discovery. --- Machine learning in high-energy physics. --- Resource-Aware Machine Learning. --- Resource-Constrained Data Analysis. --- Machine learning. --- Learning, Machine --- Artificial intelligence --- Machine theory
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