Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part III / edited by Ulf Brefeld, Edward Curry, Elizabeth Daly, Brian MacNamee, Alice Marascu, Fabio Pinelli, Michele Berlingerio, Neil Hurley.

Format:

Book

Contributor:

Brefeld, Ulf, Editor.

Curry, Edward, Editor.

Daly, Elizabeth, Editor.

MacNamee, Brian., Editor.

Marascu, Alice., Editor.

Pinelli, Fabio., Editor.

Berlingerio, Michele, Editor.

Hurley, Neil., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 11053

Lecture Notes in Artificial Intelligence, 2945-9141 ; 11053

Language:

English

Subjects (All):

Data mining.

Artificial intelligence.

Computer engineering.

Computer networks.

Social sciences-Data processing.

Data protection.

Computer crimes.

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Data and Information Security.

Computer Crime.

Local Subjects:

Data Mining and Knowledge Discovery.

Artificial Intelligence.

Computer Engineering and Networks.

Computer Application in Social and Behavioral Sciences.

Data and Information Security.

Computer Crime.

Physical Description:

1 online resource (XXXI, 706 pages) : 332 illustrations, 194 illustrations in color.

Edition:

1st ed. 2019.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-10997-4

9783030109974

Access Restriction:

Restricted for use by site license.