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.
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization / / Kailong Liu, Yujie Wang, Xin Lai
| Data Science-Based Full-Lifespan Management of Lithium-Ion Battery : Manufacturing, Operation and Reutilization / / Kailong Liu, Yujie Wang, Xin Lai |
| Autore | Liu Kailong |
| Pubbl/distr/stampa | Cham, : Springer International Publishing AG, 2022 |
| Descrizione fisica | 1 online resource (277 p.) : illustrations (chiefly color) |
| Altri autori (Persone) |
WangYujie
LaiXin |
| Collana | Green Energy and Technology |
| Soggetto topico |
Battery management systems
Lithium ion batteries |
| Soggetto non controllato |
Lithium-ion Battery
Battery Manufacturing Management Battery Operation Management Battery Recycling Management Data Science Artificial Intelligence Open Access |
| ISBN | 3-031-01340-9 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Chapter 1. Introduction to Battery Full-Lifespan Management --Chapter 2. Key Stages for Battery Full-Lifespan Management --Chapter 3. Data Science-based Battery Manufacturing Management --Chapter 4. Data Science-based Battery Operation Management I --Chapter 5. Data Science-based Battery Operation Management II --Chapter 6. Data Science-based Battery Reutilization Management --Chapter 7. The Ways Ahead. |
| Record Nr. | UNINA-9910558694203321 |
Liu Kailong
|
||
| Cham, : Springer International Publishing AG, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Datenwissenschaften und Gesellschaft : Die Genese eines transversalen Wissensfeldes / / Philippe Saner
| Datenwissenschaften und Gesellschaft : Die Genese eines transversalen Wissensfeldes / / Philippe Saner |
| Autore | Saner Philippe |
| Pubbl/distr/stampa | Bielefeld, : transcript Verlag, 2022 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 005.7 |
| Collana | Digitale Soziologie |
| Soggetto topico |
Big data
Data mining |
| Soggetto non controllato |
Datenwissenschaft
Digitalisierung Politik Arbeitsmarkt Hochschulbildung Feldtheorie Wissenschaft Big Data Universität Datengesellschaft Schweiz Wissenschaftssoziologie Wissenssoziologie Bildungsforschung Soziologie Data Science Digitalization Politics Labour Market University Education Field Theory Science University Data Society Switzerland Sociology of Science Sociology of Knowledge Educational Research Sociology |
| ISBN | 3-8394-6259-2 |
| Classificazione | AK 26600 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Nota di contenuto | Frontmatter -- Editorial -- Inhalt -- Vorwort -- Abbildungsverzeichnis -- Tabellenverzeichnis -- Danksagung -- Kapitel 1 - Einleitung -- Teil I - Grundlagen -- Kapitel 2 - Transversale Wissensgebiete als Räume zwischen Feldern -- Kapitel 3 - »Data Science« als soziales Phänomen: Genese und multiple Perspektiven -- Kapitel 4 - Forschungsdesign -- Teil II - Repräsentationen und Imaginationen von Datenwissenschaften in Arbeitsmarkt und Politik -- Einleitung -- Kapitel 5 - Repräsentationen der Datenwissenschaften im schweizerischen Arbeitsmarkt -- Kapitel 6 - Zukunftsentwürfe der Datenwissenschaften in Diskursen der Bildungs- und Forschungspolitik -- Teil III - Konstruktionen der Datenwissenschaften im akademischen Feld -- Einleitung -- Kapitel 7 - Die Konstruktion der Datenwissenschaften im akademischen Feld durch Begriffsarbeit und boundary work -- Kapitel 8 - Die Verhandlung der Datenwissenschaften in Universitäten und Hochschulen -- Kapitel 9 - Die Strukturlogik datenwissenschaftlicher Curricula -- Kapitel 10 - Die Suche nach den richtigen Kompetenzen -- Teil IV - Schlussbetrachtungen -- Kapitel 11 - Synthese -- Bibliografie -- Anhang |
| Record Nr. | UNISA-996483168603316 |
|
Saner Philippe
|
||
| Bielefeld, : transcript Verlag, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Datenwissenschaften und Gesellschaft : Die Genese eines transversalen Wissensfeldes / / Philippe Saner
| Datenwissenschaften und Gesellschaft : Die Genese eines transversalen Wissensfeldes / / Philippe Saner |
| Autore | Saner Philippe |
| Pubbl/distr/stampa | Bielefeld, : transcript Verlag, 2022 |
| Descrizione fisica | 1 online resource (320 pages) |
| Disciplina | 005.7 |
| Collana | Digitale Soziologie |
| Soggetto topico |
Big data
Data mining |
| Soggetto non controllato |
Datenwissenschaft
Digitalisierung Politik Arbeitsmarkt Hochschulbildung Feldtheorie Wissenschaft Big Data Universität Datengesellschaft Schweiz Wissenschaftssoziologie Wissenssoziologie Bildungsforschung Soziologie Data Science Digitalization Politics Labour Market University Education Field Theory Science University Data Society Switzerland Sociology of Science Sociology of Knowledge Educational Research Sociology |
| ISBN | 3-8394-6259-2 |
| Classificazione | AK 26600 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ger |
| Nota di contenuto | Frontmatter -- Editorial -- Inhalt -- Vorwort -- Abbildungsverzeichnis -- Tabellenverzeichnis -- Danksagung -- Kapitel 1 - Einleitung -- Teil I - Grundlagen -- Kapitel 2 - Transversale Wissensgebiete als Räume zwischen Feldern -- Kapitel 3 - »Data Science« als soziales Phänomen: Genese und multiple Perspektiven -- Kapitel 4 - Forschungsdesign -- Teil II - Repräsentationen und Imaginationen von Datenwissenschaften in Arbeitsmarkt und Politik -- Einleitung -- Kapitel 5 - Repräsentationen der Datenwissenschaften im schweizerischen Arbeitsmarkt -- Kapitel 6 - Zukunftsentwürfe der Datenwissenschaften in Diskursen der Bildungs- und Forschungspolitik -- Teil III - Konstruktionen der Datenwissenschaften im akademischen Feld -- Einleitung -- Kapitel 7 - Die Konstruktion der Datenwissenschaften im akademischen Feld durch Begriffsarbeit und boundary work -- Kapitel 8 - Die Verhandlung der Datenwissenschaften in Universitäten und Hochschulen -- Kapitel 9 - Die Strukturlogik datenwissenschaftlicher Curricula -- Kapitel 10 - Die Suche nach den richtigen Kompetenzen -- Teil IV - Schlussbetrachtungen -- Kapitel 11 - Synthese -- Bibliografie -- Anhang |
| Record Nr. | UNINA-9910591164703321 |
|
Saner Philippe
|
||
| Bielefeld, : transcript Verlag, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
From Opinion Mining to Financial Argument Mining
| From Opinion Mining to Financial Argument Mining |
| Autore | Chen Chung-Chi |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (102 pages) |
| Altri autori (Persone) |
HuangHen-Hsen
ChenHsin-Hsi |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Natural language & machine translation
Data mining Algorithms & data structures Artificial intelligence Information technology: general issues |
| Soggetto non controllato |
Natural Language Processing (NLP)
Data Mining and Knowledge Discovery Data Structures and Information Theory Artificial Intelligence Computer Applications Data Science Computer and Information Systems Applications Open Access financial opinion mining text mining in finance financial technology application FinTech argument mining in finance opinion quality evaluation numeral understanding Natural language & machine translation Data mining Expert systems / knowledge-based systems Algorithms & data structures Information theory Information technology: general issues |
| ISBN | 981-16-2881-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464443103316 |
|
Chen Chung-Chi
|
||
| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
From Opinion Mining to Financial Argument Mining
| From Opinion Mining to Financial Argument Mining |
| Autore | Chen Chung-Chi |
| Pubbl/distr/stampa | Springer Nature, 2021 |
| Descrizione fisica | 1 online resource (102 pages) |
| Altri autori (Persone) |
HuangHen-Hsen
ChenHsin-Hsi |
| Collana | SpringerBriefs in Computer Science |
| Soggetto topico |
Natural language & machine translation
Data mining Algorithms & data structures Artificial intelligence Information technology: general issues |
| Soggetto non controllato |
Natural Language Processing (NLP)
Data Mining and Knowledge Discovery Data Structures and Information Theory Artificial Intelligence Computer Applications Data Science Computer and Information Systems Applications Open Access financial opinion mining text mining in finance financial technology application FinTech argument mining in finance opinion quality evaluation numeral understanding Natural language & machine translation Data mining Expert systems / knowledge-based systems Algorithms & data structures Information theory Information technology: general issues |
| ISBN | 981-16-2881-5 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910482868303321 |
|
Chen Chung-Chi
|
||
| Springer Nature, 2021 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA)
| Helmholtz Portfolio Theme Large-Scale Data Management and Analysis (LSDMA) |
| Autore | Jung Christopher [Hrsg.]Meyer, Jörg [Hrsg.]Streit, Achim [Hrsg.] |
| Pubbl/distr/stampa | KIT Scientific Publishing, 2017 |
| Descrizione fisica | 1 electronic resource (V, 259 p. p.) |
| Soggetto non controllato |
Data Science
Datenlebenszyklus Datenmanagement data management data analysis data science Big Data Datenanalyse data life cycle |
| ISBN | 1000071931 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Helmholtz Portfolio Theme Large-Scale Data Management and Analysis |
| Record Nr. | UNINA-9910346960303321 |
|
Jung Christopher [Hrsg.]Meyer, Jörg [Hrsg.]Streit, Achim [Hrsg.]
|
||
| KIT Scientific Publishing, 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Process mining workshops : ICPM 2021 international workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021 : revised selected papers / / editors, Jorge Muñoz Gama, Xixi Lu
| Process mining workshops : ICPM 2021 international workshops, Eindhoven, The Netherlands, October 31 - November 4, 2021 : revised selected papers / / editors, Jorge Muñoz Gama, Xixi Lu |
| Autore | Munoz-Gama Jorge |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2022 |
| Descrizione fisica | 1 online resource (xiv, 410 pages) : illustrations (chiefly color) |
| Altri autori (Persone) |
Munoz-GamaJorge
LuXixi |
| Collana | Lecture notes in business information processing |
| Soggetto topico |
Data mining
Electronic data processing |
| Soggetto non controllato |
Process Mining
Process Discovery Process Analytics Process Querying Conformance Checking Predictive Process Monitoring Data Science Event Data Streaming Analytics Machine Learning Decision Support Systems Business Process Management Information Systems Petri Nets Open Access |
| ISBN | 3-030-98581-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- XES 2.0 Workshop and Survey -- Rethinking the Input for Process Mining: Insights from the XES Survey and Workshop -- 1 Introduction -- 2 XES Standard: A Brief Overview -- 3 Survey Design and Insights -- 4 Adding Context: Reflections from the XES 2.0 Workshop -- 5 Conclusion -- References -- EdbA 2021: 2nd International Workshop on Event Data and Behavioral Analytics -- Second International Workshop on Event Data and Behavioral Analytics (EdbA'21) -- Organization -- Workshop Chairs -- Program Committee -- Probability Estimation of Uncertain Process Trace Realizations -- 1 Introduction -- 2 Related Work -- 3 Running Example -- 4 Preliminaries -- 5 Method -- 6 Validation of Probability Estimates -- 7 Conclusion -- References -- Visualizing Trace Variants from Partially Ordered Event Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Visualizing Trace Variants -- 4.1 Approach -- 4.2 Formal Guarantees -- 4.3 Limitations -- 4.4 Implementation -- 5 Evaluation -- 6 Conclusion -- References -- Analyzing Multi-level BOM-Structured Event Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Methods -- 4.1 Analysis Methodology -- 4.2 M2BOM-Structured Assembly Processes -- 5 Case Study -- 6 Conclusion -- References -- Linac: A Smart Environment Simulator of Human Activities -- 1 Introduction -- 2 Existing Solutions -- 3 Proposed Simulation Solution -- 3.1 Configuration of the Smart Environment -- 3.2 Configuration of the Agents' Behavior - AIL Language -- 3.3 Simulation Execution -- 3.4 Clock Simulation -- 3.5 MQTT Output -- 4 Implementation -- 5 Evaluation -- 5.1 Configuration -- 5.2 Results -- 6 Conclusions and Future Works -- References -- Root Cause Analysis in Process Mining with Probabilistic Temporal Logic -- 1 Introduction -- 2 Related Work -- 3 The AITIA-PM Algorithm.
3.1 Background -- 3.2 Algorithmic Procedure -- 4 Demonstration -- 5 Conclusion -- References -- xPM: A Framework for Process Mining with Exogenous Data -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 A Framework for Process Mining with Exogenous Data -- 4.1 Linking -- 4.2 Slicing -- 4.3 Transformation -- 4.4 Discovery -- 4.5 Enhancing -- 5 Evaluation -- 5.1 Procedure -- 5.2 Quality Measures -- 5.3 Event Logs and Exogenous Data -- 5.4 Results and Discussion -- 6 Conclusion -- References -- A Bridging Model for Process Mining and IoT -- 1 Introduction -- 2 Background -- 2.1 IoT Ontologies -- 2.2 Business Process Context Modelling -- 3 Conceptual Ambiguity in IoT and PM -- 3.1 IoT Data -- 3.2 Context in PM vs Context in IoT -- 3.3 Process Event vs IoT Event -- 4 Connecting IoT and Process Mining: A Conceptual Model -- 5 Use Case Validation -- 6 Related Work -- 7 Conclusion -- References -- ML4PM 2021: 2nd International Workshop in Leveraging Machine Learning for Process Mining -- 2nd International Workshop in Leveraging Machine Learning for Process Mining (ML4PM 2021) -- Organization -- Workshop Chairs -- Program Committee -- Additional Reviewers -- Exploiting Instance Graphs and Graph Neural Networks for Next Activity Prediction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Building Instance Graphs -- 3.2 Data Preprocessing -- 3.3 Deep Graph Convolutional Neural Network -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusions and Future Works -- References -- Can Deep Neural Networks Learn Process Model Structure? An Assessment Framework and Analysis -- 1 Introduction -- 2 Related Work -- 3 A Framework for Assessing the Generalisation Capacity of RNNs -- 3.1 The Resampling Procedure -- 3.2 Metrics -- 4 Experimental Evaluation -- 4.1 Process Models -- 4.2 Hyperparameter Search -- 4.3 Results -- 5 Discussion. 6 Conclusion and Future Work -- References -- Remaining Time Prediction for Processes with Inter-case Dynamics -- 1 Introduction -- 2 Preliminaries and Related Work -- 2.1 Related Work -- 2.2 RTM Background -- 2.3 Performance Spectrum with Error Progression -- 3 Approach -- 3.1 Detecting Uncertain Segments -- 3.2 Identifying Inter-case Dynamics in Uncertain Segments -- 3.3 Inter-case Feature Creation -- 3.4 Predicting the Next Segment -- 3.5 Predicting Waiting Time -- 4 Evaluation -- 4.1 Experimental Setup -- 4.2 Results -- 5 Conclusion -- References -- Event Log Sampling for Predictive Monitoring -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Proposed Sampling Methods -- 5 Evaluation -- 5.1 Event Logs -- 5.2 Implementation -- 5.3 Evaluation Setting -- 5.4 Experimental Results -- 6 Discussion -- 7 Conclusion -- References -- Active Anomaly Detection for Key Item Selection in Process Auditing -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Active Anomaly Detection -- 2.3 Trace Visualisation -- 3 Active Selection Approach -- 3.1 Step One: Encode Process Data -- 3.2 Step Two: Assign Anomaly Score -- 3.3 Step Three: Actively Label Exceptions -- 4 Evaluation -- 4.1 Step One: Encode Process Data -- 4.2 Step Two: Assign Anomaly Score -- 4.3 Step Three: Actively Label Exceptions -- 4.4 Performance Results -- 5 Discussion -- 5.1 Cycle One -- 5.2 Cycle Two -- 5.3 Cycle Three -- 6 Limitations -- 7 Conclusion and Future Work -- References -- Prescriptive Process Monitoring Under Resource Constraints: A Causal Inference Approach -- 1 Introduction -- 2 Background and Related Work -- 2.1 Predictive Process Monitoring -- 2.2 Prescriptive Process Monitoring -- 2.3 Causal Inference -- 3 Approach -- 3.1 Log Preprocessing -- 3.2 Predictive Model -- 3.3 Causal Model -- 3.4 Resource Allocator -- 4 Evaluation -- 4.1 Dataset. 4.2 Experiment Setup -- 4.3 Results -- 4.4 Threats to Validity -- 5 Conclusion -- References -- Quantifying Explainability in Outcome-Oriented Predictive Process Monitoring -- 1 Introduction -- 2 Preliminaries -- 3 Explainability in OOPPM -- 3.1 Explainability Through Interpretability and Faithfulness -- 3.2 Logit Leaf Model -- 3.3 Generalized Logistic Rule Model -- 4 Experimental Evaluation -- 4.1 Benchmark Models -- 4.2 Event Logs -- 4.3 Implementation -- 4.4 Quantitative Metrics Results -- 5 Conclusion -- References -- SA4PM 2021: 2nd International Workshop on Streaming Analytics for Process Mining -- 2nd International Workshop on Streaming Analytics for Process Mining (SA4PM) -- Organization -- Workshop Chairs -- Program Committee -- Online Prediction of Aggregated Retailer Consumer Behaviour -- 1 Introduction -- 2 Framework -- 2.1 Features -- 2.2 Clustering -- 2.3 Training -- 2.4 Predicting -- 3 Experimental Evaluation -- 3.1 Experimental Setup -- 3.2 Results -- 4 Related Work -- 5 Conclusion and Future Work -- References -- PErrCas: Process Error Cascade Mining in Trace Streams -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Online Cascade Mining -- 4.1 Outlier Segment-Level Events -- 4.2 Error Cascade Construction -- 4.3 Cascade Patterns -- 5 Evaluation -- 5.1 Synthetic Data -- 5.2 Travel Reimbursement Process -- 6 Conclusion -- References -- Continuous Performance Evaluation for Business Process Outcome Monitoring -- 1 Introduction -- 2 Related Work -- 3 Continuous Prediction Evaluation Framework -- 4 Performance Evaluation Methods -- 4.1 Evaluating Performance Using a Local Timeline -- 4.2 Real-Time Model Performance -- 5 Experimental Analysis and Results -- 6 Conclusions -- References -- PQMI 2021: 6th International Workshop on Process Querying, Manipulation, and Intelligence. 6th International Workshop on Process Querying, Manipulation, and Intelligence (PQMI 2021) -- Organization -- Workshop Organizers -- Program Committee -- An Event Data Extraction Approach from SAP ERP for Process Mining -- 1 Introduction -- 2 Background -- 2.1 Object-Centric Event Logs -- 2.2 SAP: Entities and Relationships -- 3 Extracting Event Data from SAP ERP: Approach -- 3.1 Building Graphs of Relations -- 3.2 Extracting Object-Centric Event Logs -- 4 Extracting Event Data from SAP ERP: Tool -- 5 Assessment -- 5.1 Building a Graph of Relations -- 5.2 Extracting Object-Centric Event Logs -- 6 Related Work -- 7 Conclusion -- References -- Towards a Natural Language Conversational Interface for Process Mining -- 1 Introduction -- 2 Related Work -- 3 Proposed Method -- 3.1 Pre-processing and Tagging -- 3.2 Semantic Parsing -- 3.3 PM Tool Interface Mapping -- 4 Sample Questions -- 5 Proof of Concept -- 6 Conclusions and Future Work -- References -- On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization -- 1 Introduction -- 2 Related Work -- 2.1 Generalization Metric -- 2.2 Adversarial System Variant Approximation -- 3 Notations -- 4 Problem Statement -- 5 Experimental Setup -- 5.1 Sampling Parameter -- 5.2 Variant Log Size -- 5.3 Biased Variant Logs -- 6 Results -- 6.1 Sampling Parameter Results -- 6.2 Variant Log Size Results -- 6.3 Biased Variant Log Results -- 7 Conclusion -- References -- PODS4H 2021: 4th International Workshop on Process-Oriented Data Science for Healthcare -- Fourth International Workshop on Process-Oriented Data Science for Healthcare (PODS4H) -- Organization -- Workshop Chairs -- Program Committee -- Verifying Guideline Compliance in Clinical Treatment Using Multi-perspective Conformance Checking: A Case Study -- 1 Introduction -- 2 Background. 3 Research Method. |
| Record Nr. | UNISA-996464540703316 |
|
Munoz-Gama Jorge
|
||
| Cham, : Springer Nature, 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Projection-Based Clustering through Self-Organization and Swarm Intelligence [[electronic resource] ] : Combining Cluster Analysis with the Visualization of High-Dimensional Data / / by Michael Christoph Thrun
| Projection-Based Clustering through Self-Organization and Swarm Intelligence [[electronic resource] ] : Combining Cluster Analysis with the Visualization of High-Dimensional Data / / by Michael Christoph Thrun |
| Autore | Thrun Michael Christoph |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham, : Springer Nature, 2018 |
| Descrizione fisica | 1 online resource (XX, 201 p. 90 illus., 29 illus. in color.) |
| Disciplina | 006.4 |
| Soggetto topico |
Pattern recognition
Data structures (Computer science) Pattern Recognition Data Structures |
| Soggetto non controllato |
Cluster Analysis
Dimensionality Reduction Swarm Intelligence Visualization Unsupervised Machine Learning Data Science Knowledge Discovery 3D Printing Self-Organization Emergence Game Theory Advanced Analytics High-Dimensional Data Multivariate Data Analysis of Structured Data |
| ISBN | 3-658-20540-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Approaches to Unsupervised Machine Learning -- Methods of Visualization of High-Dimensional Data -- Quality Assessments of Visualizations -- Behavior-Based Systems in Data Science -- Databionic Swarm (DBS). |
| Record Nr. | UNINA-9910293141703321 |
|
Thrun Michael Christoph
|
||
| Cham, : Springer Nature, 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||