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190201 ||| eng |
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|a 9783030109974
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| 100 |
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|a Brefeld, Ulf
|e [editor]
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| 245 |
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|a Machine Learning and Knowledge Discovery in Databases
|h Elektronische Ressource
|b European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III
|c edited by Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley
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| 250 |
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|a 1st ed. 2019
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| 260 |
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|a Cham
|b Springer International Publishing
|c 2019, 2019
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| 300 |
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|a XXXI, 706 p. 332 illus., 194 illus. in color
|b online resource
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| 505 |
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|a 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 --
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| 505 |
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|a 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 --
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| 505 |
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|a 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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| 505 |
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|a 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 --
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| 505 |
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|a 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 --
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| 653 |
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|a Computer Application in Social and Behavioral Sciences
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Social sciences / Data processing
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| 653 |
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|a Computer crimes
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| 653 |
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|a Data mining
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| 653 |
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|a Computer networks
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| 653 |
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|a Computer Engineering and Networks
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| 653 |
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|a Data protection
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Computer engineering
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| 653 |
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|a Data Mining and Knowledge Discovery
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| 653 |
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|a Computer Crime
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| 653 |
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|a Data and Information Security
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| 700 |
1 |
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|a Curry, Edward
|e [editor]
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| 700 |
1 |
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|a Daly, Elizabeth
|e [editor]
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| 700 |
1 |
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|a MacNamee, Brian
|e [editor]
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
0 |
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|a Lecture Notes in Artificial Intelligence
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| 028 |
5 |
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|a 10.1007/978-3-030-10997-4
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| 856 |
4 |
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|u https://doi.org/10.1007/978-3-030-10997-4?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.312
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| 520 |
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|a 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. The total of 131 regular papers presented in part I and part II was carefully reviewed and selected from 535 submissions; there are 52 papers in the applied data science, nectar and demo track. The contributions were organized in topical sections named as follows: Part I: adversarial learning; anomaly and outlier detection; applications; classification; clustering and unsupervised learning; deep learning; ensemble methods; and evaluation. Part II: graphs; kernel methods; learning paradigms; matrix and tensor analysis; online and active learning; pattern and sequence mining; probabilistic models and statistical methods; recommender systems; and transfer learning. 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
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