E-Book, Englisch, Band 11329, 257 Seiten, eBook
Alzate / Monreale / Koprinska ECML PKDD 2018 Workshops
Erscheinungsjahr 2019
ISBN: 978-3-030-13453-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings
E-Book, Englisch, Band 11329, 257 Seiten, eBook
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-030-13453-2
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
Label Sanitization against Label Flipping Poisoning Attacks.- Limitations of the Lipschitz constant as a Defense Against Adversarial Examples.- Understanding Adversarial Space through the Lens of Attribution.- Detecting Potential Local Adversarial Examples for Human-Interpretable Defense.- Smart Cities with Deep Edges.- Computational Model for Urban Growth Using Socioeconomic Latent Parameters.- Object Geolocation from Crowdsourced Street Level Imagery.- Extending Support Vector Regression to Constraint Optimization: Application to the Reduction of Potentially Avoidable Hospitalizations.- SALER: a Data Science Solution to Detect and Prevent Corruption in Public Administration.- MaaSim: A Liveability Simulation for Improving the Quality of Life in Cities.- Designing Data-Driven Solutions to Societal Problems: Challenges and Approaches.- Host based Intrusion Detection System with Combined CNN/RNN Model.- Cyber Attacks against the PC Learning Algorithm.- Neural Networks in an AdversarialSetting and Ill-Conditioned Weight Space.- Pseudo-Random Number Generation using Generative Adversarial Networks.- Context Delegation for Context-Based Access Control.- An Information Retrieval System For CBRNe Incidents.- A Virtual Testbed for Critical Incident Investigation with Autonomous Remote Aerial Vehicle Surveying, Artificial Intelligence, and Decision Support.- Event relevancy pruning in support of energy-efficient sequential pattern mining.- How to Measure Energy Consumption in Machine Learning Algorithms.