Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I

Research output: Books and anthologiesConference proceedingsResearch

Standard

Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. / Brefeld, Ulf (Editor); Fromont, Elisa (Editor); Hotho, Andreas (Editor) et al.
Cham: Springer Nature Switzerland AG, 2020. 766 p. (Lecture notes in computer science; Vol. 11906), (Lecture Notes in Artificial Intelligence; Vol. 11906).

Research output: Books and anthologiesConference proceedingsResearch

Harvard

Brefeld, U, Fromont, E, Hotho, A, Knobbe, A, Maathuis, M & Robardet, C (eds) 2020, Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Lecture notes in computer science, vol. 11906, Lecture Notes in Artificial Intelligence, vol. 11906, vol. 1, Springer Nature Switzerland AG, Cham. https://doi.org/10.1007/978-3-030-46150-8

APA

Brefeld, U., Fromont, E., Hotho, A., Knobbe, A., Maathuis, M., & Robardet, C. (Eds.) (2020). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. (Lecture notes in computer science; Vol. 11906), (Lecture Notes in Artificial Intelligence; Vol. 11906). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-030-46150-8

Vancouver

Brefeld U, (ed.), Fromont E, (ed.), Hotho A, (ed.), Knobbe A, (ed.), Maathuis M, (ed.), Robardet C, (ed.). Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, Würzburg, Germany, September 16–20, 2019, Proceedings, Part I. Cham: Springer Nature Switzerland AG, 2020. 766 p. (Lecture notes in computer science). (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-030-46150-8

Bibtex

@book{bfaba5ddba6f4986bce99be73fa74e40,
title = "Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2019, W{\"u}rzburg, Germany, September 16–20, 2019, Proceedings, Part I",
abstract = "The proceedings contain 42 papers. The special focus in this conference is on Machine Learning and Knowledge Discovery in Databases. The topics include: Advocating for Multiple Defense Strategies Against Adversarial Examples; hybrid Connection and Host Clustering for Community Detection in Spatial-Temporal Network Data; collaborative Learning Based Effective Malware Detection System; a Hybrid Recommendation System Based on Bidirectional Encoder Representations; leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logs; a Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances; exploiting Temporal Convolution for Activity Prediction in Process Analytics; hyper-Parameter Optimization for Privacy-Preserving Record Linkage; group-Specific Training Data; reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications; scalable Blocking for Very Large Databases; address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach; linking Heterogeneous Data for Food Security Prediction; towards Better Evaluation of Multi-target Regression Models; assessing the Difficulty of Labelling an Instance in Crowdworking; experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data; assessing the Uncertainty of the Text Generating Process Using Topic Models; a Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods; interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges; Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data; practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks; what Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations; interpretable Privacy with Optimizable Utility.",
keywords = "Business informatics",
editor = "Ulf Brefeld and Elisa Fromont and Andreas Hotho and Arno Knobbe and Marloes Maathuis and C{\'e}line Robardet",
year = "2020",
doi = "10.1007/978-3-030-46150-8",
language = "English",
isbn = "978-3-030-46149-2",
volume = "1",
series = "Lecture notes in computer science",
publisher = "Springer Nature Switzerland AG",
address = "Switzerland",
note = "Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019, ECML PKDD 2019 ; Conference date: 16-09-2019 Through 20-09-2019",
url = "https://ecmlpkdd2019.org/",

}

RIS

TY - BOOK

T1 - Machine Learning and Knowledge Discovery in Databases

T2 - Joint European Conference on Machine Learning and Knowledge Discovery in Databases - ECML PKDD 2019

A2 - Brefeld, Ulf

A2 - Fromont, Elisa

A2 - Hotho, Andreas

A2 - Knobbe, Arno

A2 - Maathuis, Marloes

A2 - Robardet, Céline

PY - 2020

Y1 - 2020

N2 - The proceedings contain 42 papers. The special focus in this conference is on Machine Learning and Knowledge Discovery in Databases. The topics include: Advocating for Multiple Defense Strategies Against Adversarial Examples; hybrid Connection and Host Clustering for Community Detection in Spatial-Temporal Network Data; collaborative Learning Based Effective Malware Detection System; a Hybrid Recommendation System Based on Bidirectional Encoder Representations; leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logs; a Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances; exploiting Temporal Convolution for Activity Prediction in Process Analytics; hyper-Parameter Optimization for Privacy-Preserving Record Linkage; group-Specific Training Data; reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications; scalable Blocking for Very Large Databases; address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach; linking Heterogeneous Data for Food Security Prediction; towards Better Evaluation of Multi-target Regression Models; assessing the Difficulty of Labelling an Instance in Crowdworking; experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data; assessing the Uncertainty of the Text Generating Process Using Topic Models; a Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods; interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges; Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data; practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks; what Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations; interpretable Privacy with Optimizable Utility.

AB - The proceedings contain 42 papers. The special focus in this conference is on Machine Learning and Knowledge Discovery in Databases. The topics include: Advocating for Multiple Defense Strategies Against Adversarial Examples; hybrid Connection and Host Clustering for Community Detection in Spatial-Temporal Network Data; collaborative Learning Based Effective Malware Detection System; a Hybrid Recommendation System Based on Bidirectional Encoder Representations; leveraging Multi-target Regression for Predicting the Next Parallel Activities in Event Logs; a Multi-view Ensemble of Deep Models for the Detection of Deviant Process Instances; exploiting Temporal Convolution for Activity Prediction in Process Analytics; hyper-Parameter Optimization for Privacy-Preserving Record Linkage; group-Specific Training Data; reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications; scalable Blocking for Very Large Databases; address Validation in Transportation and Logistics: A Machine Learning Based Entity Matching Approach; linking Heterogeneous Data for Food Security Prediction; towards Better Evaluation of Multi-target Regression Models; assessing the Difficulty of Labelling an Instance in Crowdworking; experimental Evaluation of Scale, and Patterns of Systematic Inconsistencies in Google Trends Data; assessing the Uncertainty of the Text Generating Process Using Topic Models; a Ranking Stability Measure for Quantifying the Robustness of Anomaly Detection Methods; interpretable Machine Learning – A Brief History, State-of-the-Art and Challenges; Efficient Estimation of General Additive Neural Networks: A Case Study for CTG Data; practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks; what Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model Conversations; interpretable Privacy with Optimizable Utility.

KW - Business informatics

UR - http://www.scopus.com/inward/record.url?scp=85101311368&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-46150-8

DO - 10.1007/978-3-030-46150-8

M3 - Conference proceedings

SN - 978-3-030-46149-2

VL - 1

T3 - Lecture notes in computer science

BT - Machine Learning and Knowledge Discovery in Databases

PB - Springer Nature Switzerland AG

CY - Cham

Y2 - 16 September 2019 through 20 September 2019

ER -

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