Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings

Author(s)

• ECML PKDD
• Berlingerio, Michele

Bibliographic Information

Machine learning and knowledge discovery in databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018 : proceedings

Michele Berlingerio ... [et al.](Eds.)

（Lecture notes in computer science, 11051-11053 . Lecture notes in artificial intelligence）

Springer, c2019

• pt. 1
• pt. 2
• pt. 3

Other Title

ECML PKDD 2018

Note

Includes bibliographical references and index

Description and Table of Contents

Volume

pt. 1 ISBN 9783030109240

Description

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 learningensemble 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.

Table of Contents

Adversarial Learning.- Image Anomaly Detection with Generative Adversarial Networks.- Image-to-Markup Generation via Paired Adversarial Learning.- Toward an Understanding of Adversarial Examples in Clinical Trials.- ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector.- Anomaly and Outlier Detection.- GridWatch: Sensor Placement and Anomaly Detection in the Electrical Grid.- Incorporating Privileged Information to Unsupervised Anomaly Detection.- L1-Depth Revisited: A Robust Angle-based Outlier Factor in High-dimensional Space.- Beyond Outlier Detection: LookOut for Pictorial Explanation.- Scalable and Interpretable One-class SVMs with Deep Learning and Random Fourier Features.- Group Anomaly Detection using Deep Generative Models.- Applications.- A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements.- Face-Cap: Image Captioning using Facial Expression Analysis.- Pedestrian Trajectory Prediction with Structured Memory Hierarchies.- Classification.- Multiple Instance Learning with Bag-level Randomized Trees.- One-class Quantification.- Deep F-Measure Maximization in Multi-Label Classification: A Comparative Study.- Ordinal Label Proportions.- AWX: An Integrated Approach to Hierarchical-Multilabel Classification.- Clustering and Unsupervised Learning.- Clustering in the Presence of Concept Drift.- Time Warp Invariant Dictionary Learning for Time Series Clustering.- How Your Supporters and Opponents Define Your Interestingness.- Deep Learning.- Efficient Decentralized Deep Learning by Dynamic Model Averaging.- Using Supervised Pretraining to Improve Generalization of Neural Networks on Binary Classification Problems.- Towards Efficient Forward Propagation on Resource-Constrained Systems.- Auxiliary Guided Autoregressive Variational Autoencoders.- Cooperative Multi-Agent Policy Gradient.- Parametric t-Distributed Stochastic Exemplar-centered Embedding.- Joint autoencoders: a flexible meta-learning framework.- Privacy Preserving Synthetic Data Release Using Deep Learning.- On Finer Control of Information Flow in LSTMs.- MaxGain: Regularisation of Neural Networks by Constraining Activation Magnitudes.- Ontology alignment based on word embedding and random forest classification.- Domain Adaption in One-Shot Learning.- Ensemble Methods.- Axiomatic Characterization of AdaBoost and the Multiplicative Weight Update Procedure.- Modular Dimensionality Reduction.- Constructive Aggregation and its Application to Forecasting with Dynamic Ensembles.- MetaBags: Bagged Meta-Decision Trees for Regression.- Evaluation.- Visualizing the Feature Importance for Black Box Models.- Efficient estimation of AUC in a sliding window.- Controlling and visualizing the precision-recall tradeoff for external performance indices.- Evaluation Procedures for Forecasting with Spatio-Temporal Data.- A Blended Metric for Multi-label Optimisation and Evaluation.

Volume

pt. 2 ISBN 9783030109271

Description

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 learningensemble 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.

Table of Contents

Graphs.- Temporally Evolving Community Detection and Prediction in Content-Centric Networks.- Local Topological Data Analysis to Uncover the Global Structure of Data Approaching Graph-Structured Topologies.- Similarity Modeling on Heterogeneous Networks via Automatic Path Discovery.- Dynamic hierarchies in temporal directed networks.- Risk-Averse Matchings over Uncertain Graph Databases.- Discovering Urban Travel Demands through Dynamic Zone Correlation in Location-Based Social Networks.- Social-Affiliation Networks: Patterns and the SOAR Model.- ONE-M: Modeling the Co-evolution of Opinions and Network Connections.- Think before You Discard: Accurate Triangle Counting in Graph Streams with Deletions.- Semi-Supervised Blockmodelling with Pairwise Guidance.- Kernel Methods.- Large-scale Nonlinear Variable Selection via Kernel Random Features.- Fast and Provably Effective Multi-view Classification with Landmark-based SVM.- Nystroem-SGD: Fast Learning of Kernel-Classifiers with Conditioned Stochastic Gradient Descent.- Learning Paradigms.- Hyperparameter Learning for Conditional Kernel Mean Embeddings with Rademacher Complexity Bounds.- Deep Learning Architecture Search by Neuro-Cell-based Evolution with Function-Preserving Mutations.- VC-Dimension Based Generalization Bounds for Relational Learning.- Robust Super-Level Set Estimation using Gaussian Processes.- Robust Super-Level Set Estimation using Gaussian Processes.- Scalable Nonlinear AUC Maximization Methods.- Matrix and Tensor Analysis.- Lambert Matrix Factorization.- Identifying and Alleviating Concept Drift in Streaming Tensor Decomposition.- MASAGA: A Linearly-Convergent Stochastic First-Order Method for Optimization on Manifolds.- Block CUR: Decomposing Matrices using Groups of Columns.- Online and Active Learning.- SpectralLeader: Online Spectral Learning for Single Topic Models.- Online Learning of Weighted Relational Rules for Complex Event Recognition.- Toward Interpretable Deep Reinforcement Learning with Linear Model U-Trees.- Online Feature Selection by Adaptive Sub-gradient Methods.- Frame-based Optimal Design.- Hierarchical Active Learning with Proportion Feedback on Regions.- Pattern and Sequence Mining.- An Efficient Algorithm for Computing Entropic Measures of Feature Subsets.- Anytime Subgroup Discovery in Numerical Domains with Guarantees.- Discovering Spatio-Temporal Latent Influence in Geographical Attention Dynamics.- Mining Periodic Patterns with a MDL Criterion.- Revisiting Conditional Functional Dependency Discovery: Splitting the "C" from the "FD".- Sqn2Vec: Learning Sequence Representation via Sequential Patterns with a Gap Constraint.- Mining Tree Patterns with Partially Injective Homomorphisms.- Probabilistic Models and Statistical Methods.- Variational Bayes for Mixture Models with Censored Data.- Exploration Enhanced Expected Improvement for Bayesian Optimization.- A Left-to-right Algorithm for Likelihood Estimation in Gamma-Poisson Factor Analysis.- Causal Inference on Multivariate and Mixed-Type Data.- Recommender Systems.- POLAR: Attention-based CNN for One-shot Personalized Article Recommendation.- Learning Multi-granularity Dynamic Network Representations for Social Recommendation.- GeoDCF: Deep Collaborative Filtering with Multifaceted Contextual Information in Location-based Social Networks.- Personalized Thread Recommendation for MOOC Discussion Forums.- Inferring Continuous Latent Preference on Transition Intervals for Next Point-of-Interest Recommendation.- Transfer Learning.- Feature Selection for Unsupervised Domain Adaptation using Optimal Transport.- Towards more Reliable Transfer Learning.- Differentially Private Hypothesis Transfer Learning.- Information-theoretic Transfer Learning framework for Bayesian Optimisation.- A Unified Framework for Domain Adaptation using Metric Learning on Manifolds.

Volume

pt. 3 ISBN 9783030109967

Description

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.

Table of 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.

by "Nielsen BookData"

• NCID: BB27645358
• ISBN:
  • 9783030109240
  • 9783030109271
  • 9783030109967
• Country Code: sz
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cham
• Pages/Volumes: 3 v.
• Size: 24 cm
• Classification:
  • DC23 : 006.31
• Subject Headings:
  • LCSH : Machine learning -- Congresses
  • LCSH : Data mining -- Congresses
  • FREE : Knowledge discovery
  • FREE : Databases
• Parent Bibliography ID:
  • BA00009279