top

  Info

  • Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.

  Info

  • Utilizzare questo link per rimuovere la selezione effettuata.
Machine learning under resource constraints Fundamentals / / edited by Katharina Morik and Peter Marwedel
Machine learning under resource constraints Fundamentals / / edited by Katharina Morik and Peter Marwedel
Edizione [1st ed.]
Pubbl/distr/stampa Berlin ; ; Boston : , : De Gruyter, , [2023]
Descrizione fisica 1 online resource (xiii, 491 pages) : illustrations (chiefly colour)
Disciplina 006.31
Collana De Gruyter STEM
Soggetto topico Machine learning
SCIENCE / Chemistry / General
Soggetto non controllato 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
ISBN 3-11-078594-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; 1 Introduction / Katharina Morik, Jian-Jia Chen -- ; 1.1 Embedded Systems and Sustainability -- ; 1.2 The Energy Consumption of Machine Learning -- ; 1.3 Memory Demands of Machine Learning -- ; 1.4 Structure of this Book -- ; 2 Data Gathering and Resource Measuring -- ; 2.1 Declarative Stream-Based Acquisition and Processing of OS Data with kCQL / Christoph Borchert, Jochen Streicher, Alexander Lochmann,Olaf Spinczyk -- ; 2.2 PhyNetLab Test Bed / Mojtaba Masoudinejad, Markus Buschhoff -- ; 2.3 Zero-Power/Low-Power Sensing / Andres Gomez, Lars Suter, Simon Mayer -- ; 3 Streaming Data, Small Devices -- ; 3.1 Summary Extraction from Streams / Sebastian Buschjäger, Katharina Morik -- ; 3.2 Coresets and Sketches for Regression Problems on Data Streams and Distributed Data / Alexander Munteanu -- ; 4 Structured Data -- ; 4.1 Spatio-Temporal Random Fields / Nico Piatkowski, Katharina Morik -- ; 4.2 The Weisfeiler-Leman Method for Machine Learning with Graphs / Nils Kriege, Christopher Morris -- ; 4.3 Deep Graph Representation Learning / Matthias Fey, Frank Weichert -- ; 4.4 High-Quality Parallel Max-Cut Approximation Algorithms for Shared Memory / Nico Bertram, Jonas Ellert, Johannes Fischer -- ; 4.5 Millions of Formulas / Lukas Pfahler -- ; 5 Cluster Analysis -- ; 5.1 Sparse Partitioning Around Medoids / Lars Lenssen, Erich Schubert -- ; 5.2 Clustering of Polygonal Curves and Time Series / Amer Krivošija -- ; 5.3 Data Aggregation for Hierarchical Clustering / Erich Schubert, Andreas Lang -- ; 5.4 Matrix Factorization with Binary Constraints / Sibylle Hess ; 6 Hardware-Aware Execution -- ; 6.1 FPGA-Based Backpropagation Engine for Feed-Forward Neural Networks / Wayne Luk, Ce Guo -- ; 6.2 Processor-Specific Code Transformation / Henning Funke, Jens Teubner -- ; 6.3 Extreme Multicore Classification / Erik Schultheis, Rohit Babbar -- ; 6.4 Optimization of ML on Modern Multicore Systems / Helena Kotthaus, Peter Marwedel -- 7 Memory Awareness -- ; 7.1 Efficient Memory Footprint Reduction / Helena Kotthaus, Peter Marwedel -- ; 7.2 Machine Learning Based on Emerging Memories / Mikail Yayla, Sebastian Buschjäger, Hussam Amrouch -- ; 7.3 Cache-Friendly Execution of Tree Ensembles / Sebastian Buschjäger, Kuan-Hsun Chen -- ; 8 Communication Awareness -- ; 8.1 Timing-Predictable Learning and Multiprocessor Synchronization / Kuan-Hsun Chen, Junjie Shi -- ; 8.2 Communication Architecture for Heterogeneous Hardware / Henning Funke, Jens Teubner -- ; 9 Energy Awareness -- ; 9.1 Integer Exponential Families / Nico Piatkowski -- ; 9.2 Power Consumption Analysis and Uplink Transmission Power / Robert Falkenberg.
Record Nr. UNISA-996503570003316
Berlin ; ; Boston : , : De Gruyter, , [2023]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Machine learning under resource constraints Fundamentals / / edited by Katharina Morik and Peter Marwedel
Machine learning under resource constraints Fundamentals / / edited by Katharina Morik and Peter Marwedel
Edizione [1st ed.]
Pubbl/distr/stampa Berlin ; ; Boston : , : De Gruyter, , [2023]
Descrizione fisica 1 online resource (xiii, 491 pages) : illustrations (chiefly colour)
Disciplina 006.31
Collana De Gruyter STEM
Soggetto topico Machine learning
SCIENCE / Chemistry / General
Soggetto non controllato 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
ISBN 3-11-078594-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto ; 1 Introduction / Katharina Morik, Jian-Jia Chen -- ; 1.1 Embedded Systems and Sustainability -- ; 1.2 The Energy Consumption of Machine Learning -- ; 1.3 Memory Demands of Machine Learning -- ; 1.4 Structure of this Book -- ; 2 Data Gathering and Resource Measuring -- ; 2.1 Declarative Stream-Based Acquisition and Processing of OS Data with kCQL / Christoph Borchert, Jochen Streicher, Alexander Lochmann,Olaf Spinczyk -- ; 2.2 PhyNetLab Test Bed / Mojtaba Masoudinejad, Markus Buschhoff -- ; 2.3 Zero-Power/Low-Power Sensing / Andres Gomez, Lars Suter, Simon Mayer -- ; 3 Streaming Data, Small Devices -- ; 3.1 Summary Extraction from Streams / Sebastian Buschjäger, Katharina Morik -- ; 3.2 Coresets and Sketches for Regression Problems on Data Streams and Distributed Data / Alexander Munteanu -- ; 4 Structured Data -- ; 4.1 Spatio-Temporal Random Fields / Nico Piatkowski, Katharina Morik -- ; 4.2 The Weisfeiler-Leman Method for Machine Learning with Graphs / Nils Kriege, Christopher Morris -- ; 4.3 Deep Graph Representation Learning / Matthias Fey, Frank Weichert -- ; 4.4 High-Quality Parallel Max-Cut Approximation Algorithms for Shared Memory / Nico Bertram, Jonas Ellert, Johannes Fischer -- ; 4.5 Millions of Formulas / Lukas Pfahler -- ; 5 Cluster Analysis -- ; 5.1 Sparse Partitioning Around Medoids / Lars Lenssen, Erich Schubert -- ; 5.2 Clustering of Polygonal Curves and Time Series / Amer Krivošija -- ; 5.3 Data Aggregation for Hierarchical Clustering / Erich Schubert, Andreas Lang -- ; 5.4 Matrix Factorization with Binary Constraints / Sibylle Hess ; 6 Hardware-Aware Execution -- ; 6.1 FPGA-Based Backpropagation Engine for Feed-Forward Neural Networks / Wayne Luk, Ce Guo -- ; 6.2 Processor-Specific Code Transformation / Henning Funke, Jens Teubner -- ; 6.3 Extreme Multicore Classification / Erik Schultheis, Rohit Babbar -- ; 6.4 Optimization of ML on Modern Multicore Systems / Helena Kotthaus, Peter Marwedel -- 7 Memory Awareness -- ; 7.1 Efficient Memory Footprint Reduction / Helena Kotthaus, Peter Marwedel -- ; 7.2 Machine Learning Based on Emerging Memories / Mikail Yayla, Sebastian Buschjäger, Hussam Amrouch -- ; 7.3 Cache-Friendly Execution of Tree Ensembles / Sebastian Buschjäger, Kuan-Hsun Chen -- ; 8 Communication Awareness -- ; 8.1 Timing-Predictable Learning and Multiprocessor Synchronization / Kuan-Hsun Chen, Junjie Shi -- ; 8.2 Communication Architecture for Heterogeneous Hardware / Henning Funke, Jens Teubner -- ; 9 Energy Awareness -- ; 9.1 Integer Exponential Families / Nico Piatkowski -- ; 9.2 Power Consumption Analysis and Uplink Transmission Power / Robert Falkenberg.
Record Nr. UNINA-9910774817103321
Berlin ; ; Boston : , : De Gruyter, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer
Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer
Edizione [1st ed.]
Pubbl/distr/stampa Berlin ; ; Boston : , : De Gruyter, , [2022]
Descrizione fisica 1 online resource (VIII, 470 p.)
Disciplina 006.31
Collana De Gruyter STEM
Soggetto topico SCIENCE / Chemistry / General
Soggetto non controllato 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
ISBN 3-11-078598-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- 1 Editorial -- 2 Health / Medicine -- 2.1 Machine Learning in Medicine -- 2.2 Virus Detection -- 2.3 Cancer Diagnostics and Therapy from Molecular Data -- 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction -- 2.5 Survival Prediction and Model Selection -- 2.6 Protein Complex Similarity -- 3 Industry 4.0 -- 3.1 Keynote on Industry 4.0 -- 3.2 Quality Assurance in Interlinked Manufacturing Processes -- 3.3 Label Proportion Learning -- 3.4 Simulation and Machine Learning -- 3.5 High-Precision Wireless Localization -- 3.6 Indoor Photovoltaic Energy Harvesting -- 3.7 Micro-UAV Swarm Testbed for Indoor Applications -- 4 Smart City and Traffic -- 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors -- 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows -- 4.3 Green Networking and Resource Constrained Clients for Smart Cities -- 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing -- 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata -- 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms -- 5 Communication Networks -- 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum -- 5.2 Resource-Efficient Vehicle-to-Cloud Communications -- 5.3 Mobile-Data Network Analytics Highly Reliable Networks -- 5.4 Machine Learning-Enabled 5G Network Slicing -- 5.5 Potential of Millimeter Wave Communications -- 6 Privacy -- 6.1 Keynote: Construction of Inference-Proof Agent Interactions -- Bibliography -- Index -- List of Contributors
Record Nr. UNISA-996503570503316
Berlin ; ; Boston : , : De Gruyter, , [2022]
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer
Machine Learning under Resource Constraints. Applications / / ed. by Katharina Morik, Christian Wietfeld, Jörg Rahnenführer
Edizione [1st ed.]
Pubbl/distr/stampa Berlin ; ; Boston : , : De Gruyter, , [2022]
Descrizione fisica 1 online resource (VIII, 470 p.)
Disciplina 006.31
Collana De Gruyter STEM
Soggetto topico SCIENCE / Chemistry / General
Soggetto non controllato 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
ISBN 3-11-078598-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Frontmatter -- Contents -- 1 Editorial -- 2 Health / Medicine -- 2.1 Machine Learning in Medicine -- 2.2 Virus Detection -- 2.3 Cancer Diagnostics and Therapy from Molecular Data -- 2.4 Bayesian Analysis for Dimensionality and Complexity Reduction -- 2.5 Survival Prediction and Model Selection -- 2.6 Protein Complex Similarity -- 3 Industry 4.0 -- 3.1 Keynote on Industry 4.0 -- 3.2 Quality Assurance in Interlinked Manufacturing Processes -- 3.3 Label Proportion Learning -- 3.4 Simulation and Machine Learning -- 3.5 High-Precision Wireless Localization -- 3.6 Indoor Photovoltaic Energy Harvesting -- 3.7 Micro-UAV Swarm Testbed for Indoor Applications -- 4 Smart City and Traffic -- 4.1 Inner-City Traffic Flow Prediction with Sparse Sensors -- 4.2 Privacy-Preserving Detection of Persons and Classification of Vehicle Flows -- 4.3 Green Networking and Resource Constrained Clients for Smart Cities -- 4.4 Vehicle to Vehicle Communications: Machine Learning-Enabled Predictive Routing -- 4.5 Modelling of Hybrid Vehicular Traffic with Extended Cellular Automata -- 4.6 Embedded Crowdsensing for Pavement Monitoring and its Incentive Mechanisms -- 5 Communication Networks -- 5.1 Capacity Analysis of IoT Networks in the Unlicensed Spectrum -- 5.2 Resource-Efficient Vehicle-to-Cloud Communications -- 5.3 Mobile-Data Network Analytics Highly Reliable Networks -- 5.4 Machine Learning-Enabled 5G Network Slicing -- 5.5 Potential of Millimeter Wave Communications -- 6 Privacy -- 6.1 Keynote: Construction of Inference-Proof Agent Interactions -- Bibliography -- Index -- List of Contributors
Record Nr. UNINA-9910774815903321
Berlin ; ; Boston : , : De Gruyter, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Vorkurs Informatik : Der Einstieg ins Informatikstudium / / von Heinrich Müller, Frank Weichert
Vorkurs Informatik : Der Einstieg ins Informatikstudium / / von Heinrich Müller, Frank Weichert
Autore Müller Heinrich
Edizione [5th ed. 2017.]
Pubbl/distr/stampa Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2017
Descrizione fisica 1 online resource (XIV, 392 S. 151 Abb.)
Disciplina 005.13
Soggetto topico Programming languages (Electronic computers)
Algorithms
Computer programming
Software engineering
Programming Languages, Compilers, Interpreters
Algorithm Analysis and Problem Complexity
Programming Techniques
Software Engineering
ISBN 3-658-16141-8
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ger
Nota di contenuto Was ist Informatik? -- Programmierung -- Erweiterte Programmierkonzepte -- Algorithmen und Datenstrukturen -- Vom Programm zum Rechner.
Record Nr. UNINA-9910484245603321
Müller Heinrich  
Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui