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