Machine Learning and Knowledge Discovery in Databases : 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.

Format:

Book

Contributor:

Berlingerio, Michele, Editor.

Bonchi, Francesco, Editor.

Gärtner, Thomas, Editor.

Hurley, Neil., Editor.

Ifrim, Georgiana., Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 11051

Lecture Notes in Artificial Intelligence, 2945-9141 ; 11051

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Computer vision.

Social sciences-Data processing.

Computers.

Data protection.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Computer Vision.

Computer Application in Social and Behavioral Sciences.

Computing Milieux.

Data and Information Security.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Computer Vision.

Computer Application in Social and Behavioral Sciences.

Computing Milieux.

Data and Information Security.

Physical Description:

1 online resource (XXXVIII, 740 pages) : 451 illustrations, 159 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 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.

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.

Other Format:

Printed edition:

ISBN:

978-3-030-10925-7

9783030109257

Access Restriction:

Restricted for use by site license.