ECML PKDD 2018 Workshops : Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings /: Nemesis 2018, UrbReas 2018, SoGood 2018, IWAISe 2018, and Green Data Mining 2018, Dublin, Ireland, September 10-14, 2018, Proceedings. (2019)

• Record Type: Book
• Further Information: Note: Carlos Alzate, Anna Monreale [and others].
• Editors: Alzate, Carlos; Monreale, Anna
• Other Names: ECML PKDD (Conference), 18th; Nemesis (Workshop); UrbReas (Workshop); SoGood (Workshop); IWAISe (Workshop); Green Data Mining (Workshop)
• Contents: 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 Adversarial Setting 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.
• Publisher Details: Cham, Switzerland : Springer
• Publication Date: 2019
• Extent: 1 online resource (x, 257 pages), illustrations (some color)
• Subjects: 006.3/1; Machine learning -- Congresses; Data mining -- Congresses; Electronic books
• Languages: English
• ISBNs: 9783030134532; 3030134539
• Related ISBNs: 9783030134525
• Notes: Note: Online resource; title from PDF title page (SpringerLink, viewed February 20, 2019).
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.