Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III - Taschenbuch
2019, ISBN: 3030109968
[EAN: 9783030109967], Nieuw boek, [SC: 14.1], [PU: Springer-Verlag Gmbh Jan 2019], DATA MINING (EDV); EXPERTENSYSTEM; WISSENSBASIERTES SYSTEM; DATENSICHERHEIT; DATENSICHERUNG; SICHERHEIT … Mehr…
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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III - Taschenbuch
2019, ISBN: 3030109968
[EAN: 9783030109967], Neubuch, [SC: 0.0], [PU: Springer-Verlag Gmbh Jan 2019], DATA MINING (EDV); EXPERTENSYSTEM; WISSENSBASIERTES SYSTEM; DATENSICHERHEIT; DATENSICHERUNG; SICHERHEIT / HA… Mehr…
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Machine Learning and Knowledge Discovery in Databases | European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III | Ulf Brefeld (u. a.) | Taschenbuch | XXXI - Taschenbuch
2019, ISBN: 9783030109967
[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…
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Machine Learning and Knowledge Discovery in Databases / European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III / Ulf Brefeld (u. a.) / Taschenbuch / XXXI - Taschenbuch
2019, ISBN: 9783030109967
[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…
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2019, ISBN: 3030109968
The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, h… Mehr…
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Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III - Taschenbuch
2019, ISBN: 3030109968
[EAN: 9783030109967], Nieuw boek, [SC: 14.1], [PU: Springer-Verlag Gmbh Jan 2019], DATA MINING (EDV); EXPERTENSYSTEM; WISSENSBASIERTES SYSTEM; DATENSICHERHEIT; DATENSICHERUNG; SICHERHEIT … Mehr…
Ulf Brefeld:
Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III - Taschenbuch2019, ISBN: 3030109968
[EAN: 9783030109967], Neubuch, [SC: 0.0], [PU: Springer-Verlag Gmbh Jan 2019], DATA MINING (EDV); EXPERTENSYSTEM; WISSENSBASIERTES SYSTEM; DATENSICHERHEIT; DATENSICHERUNG; SICHERHEIT / HA… Mehr…
Machine Learning and Knowledge Discovery in Databases | European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III | Ulf Brefeld (u. a.) | Taschenbuch | XXXI - Taschenbuch
2019
ISBN: 9783030109967
[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…
Machine Learning and Knowledge Discovery in Databases / European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III / Ulf Brefeld (u. a.) / Taschenbuch / XXXI - Taschenbuch
2019, ISBN: 9783030109967
[ED: Taschenbuch], [PU: Springer-Verlag GmbH], The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Know… Mehr…
2019, ISBN: 3030109968
The three volume proceedings LNAI 11051 ¿ 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, h… Mehr…
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Detailangaben zum Buch - Machine Learning and Knowledge Discovery in Databases
EAN (ISBN-13): 9783030109967
ISBN (ISBN-10): 3030109968
Taschenbuch
Erscheinungsjahr: 2019
Herausgeber: Springer International Publishing
Buch in der Datenbank seit 2019-02-09T16:57:32+01:00 (Vienna)
Detailseite zuletzt geändert am 2023-11-16T21:08:20+01:00 (Vienna)
ISBN/EAN: 9783030109967
ISBN - alternative Schreibweisen:
3-030-10996-8, 978-3-030-10996-7
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: berling, berlinger, pine, michel pinel, daly, curry, pinelli
Titel des Buches: book discovery, book ireland, knowledge discovery databases, discovery dublin, knowledge learning
Daten vom Verlag:
Autor/in: Ulf Brefeld; Edward Curry; Elizabeth Daly; Brian MacNamee; Alice Marascu; Fabio Pinelli; Michele Berlingerio; Neil Hurley
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III
Verlag: Springer; Springer International Publishing
706 Seiten
Erscheinungsjahr: 2019-01-18
Cham; CH
Gedruckt / Hergestellt in Niederlande.
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
POD
XXXI, 706 p. 332 illus., 194 illus. in color.
BC; Hardcover, Softcover / Informatik, EDV/Informatik; Data Mining; Verstehen; artificial intelligence; bayesian networks; classification; data mining; Human-Computer Interaction (HCI); internet; learning algorithms; machine learning; natural language processing systems; neural networks; semantics; sensor; sensors; signal processing; social networks; ubiquitous computing; user interfaces; wireless networks; wireless telecommunication systems; World Wide Web; Data Mining and Knowledge Discovery; Artificial Intelligence; Computer Engineering and Networks; Computer Application in Social and Behavioral Sciences; Data and Information Security; Computer Crime; Wissensbasierte Systeme, Expertensysteme; Künstliche Intelligenz; Computerhardware; Computer-Anwendungen in den Sozial- und Verhaltenswissenschaften; Computersicherheit; Netzwerksicherheit; Computerkriminalität, Hacking; EA
The three volume proceedings LNAI 11051 – 11053 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018, held in Dublin, Ireland, in September 2018.
Part III: ADS data science applications; ADS e-commerce;ADS engineering and design; ADS financial and security; ADS health; ADS sensing and positioning; nectar track; and demo track.
ADS Data Science Applications.- Neural Article Pair Modeling for Wikipedia Sub-article Matching.- LinNet: Probabilistic Lineup Evaluation Through Network Embedding.- Improving Emotion Detection with Sub-clip Boosting.- Machine Learning for Targeted Assimilation of Satellite Data.- From Empirical Analysis to Public Policy: Evaluating Housing Systems for Homeless Youth.- Discovering Groups of Signals in In-Vehicle Network Traces for Redundancy Detection and Functional Grouping.- ADS E-commerce.- SPEEDING up the Metabolism in E-commerce by Reinforcement Mechanism DESIGN.- Intent-aware Audience Targeting for Ride-hailing Service.- A Recurrent Neural Network Survival Model: Predicting Web User Return Time.- Implicit Linking of Food Entities in Social Media.- A Practical Deep Online Ranking System in E-commerce Recommendation.- ADS Engineering and Design.- ST-DenNetFus: A New Deep Learning Approach for Network Demand Prediction.- Automating Layout Synthesis with Constructive Preference Elicitation.- Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems.- Learning Cheap and Novel Flight Itineraries.- Towards Resource-Efficient Classifiers for Always-On Monitoring.- ADS Financial / Security.- Uncertainty Modelling in Deep Networks: Forecasting Short and Noisy Series.- Using Reinforcement Learning to Conceal Honeypot Functionality.- Flexible Inference for Cyberbully Incident Detection.- Solving the \\false positives" problem in fraud prediction - Automated Data Science at an Industrial Scale.- Learning Tensor-based Representations from Brain-Computer Interface Data for Cybersecurity.- ADS Health.- Can We Assess Mental Health through Social Media and Smart Devices? Addressing Bias in Methodology and Evaluation.- AMIE: Automatic Monitoring of Indoor Exercises.- Rough Set Theory as a Data Mining Technique: A Case Study in Epidemiology and Cancer Incidence Prediction.- Selecting Influenza Mitigation Strategies Using Bayesian Bandits.- Hypotensive Episode Prediction in ICUs via Observation Window Splitting.- Equipment Health Indicator Learning using Deep Reinforcement Learning.- ADS Sensing/Positioning.- PBE: Driver Behavior Assessment Beyond Trajectory Profiling.- Accurate WiFi-based Indoor Positioning with Continuous Location Sampling.- Human Activity Recognition with Convolutional Neural Networks.- Urban sensing for anomalous event detection.- Combining Bayesian Inference and Clustering for Transport Mode Detection from Sparse and Noisy Geolocation Data.- CentroidNet: A Deep Neural Network for Joint Object Localization and Counting.- Deep Modular Multimodal Fusion on Multiple Sensors for Volcano Activity Recognition.- Nectar Track.- Matrix Completion under Interval Uncertainty.- A two-step approach for the prediction of mood levels based on diary data.- Best Practices to Train Deep Models on Imbalanced Datasets - A Case Study on Animal Detection in Aerial Imagery.- Deep Query Ranking for Question Answering over Knowledge Bases.- Machine Learning Approaches to Hybrid Music Recommender Systems.- Demo Track.- IDEA: An Interactive Dialogue Translation Demo System Using Furhat Robots.- RAPID: Real-time Analytics Platform for Interactive Data Mining.- COBRASTS: A new approach to Semi-Supervised Clustering of Time Series.- pysubgroup: Easy-to-use Subgroup Discovery in Python.- An Advert Creation System for Next-Gen Publicity.- VHI : Valve Health Identification for the Maintenance of Subsea Industrial Equipment.- Tiler: Software for Human-Guided Data Exploration.- ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio.- ClaRe: Classification and Regression Tool for Multivariate Time Series.- Industrial Memories: Exploring the Findings of Government Inquiries with Neural Word Embedding and Machine Learning.- Monitoring Emergency First Responders' Activities via Gradient Boosting and Inertial Sensor Data.- Visualizing Multi-Document Semantics via Open Domain Information Extraction.
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