Title | Machine Learning and Knowledge Discovery in Databases [electronic resource] : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I / edited by Michele Berlingerio, Francesco Bonchi, Thomas Gärtner, Neil Hurley, Georgiana Ifrim |
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Imprint | Cham : Springer International Publishing : Imprint: Springer, 2019 |
Edition | 1st ed. 2019 |
Connect to | https://doi.org/10.1007/978-3-030-10925-7 |
Descript | XXXVIII, 740 p. 451 illus., 159 illus. in color. online resource |
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