Machine Learning and Knowledge Discovery in Databases : European Conference, ECML PKDD 2018, Dublin, Ireland, September 10-14, 2018, Proceedings, Part II / 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 (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 11052.

Lecture Notes in Artificial Intelligence ; 11052

Language:

English

Subjects (All):

Artificial intelligence.

Data mining.

Optical data processing.

Application software.

Computers.

Data protection.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Image Processing and Computer Vision.

Computer Appl. in Social and Behavioral Sciences.

Computing Milieux.

Security.

Local Subjects:

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Image Processing and Computer Vision.

Computer Appl. in Social and Behavioral Sciences.

Computing Milieux.

Security.

Physical Description:

1 online resource (XXX, 866 pages) : 463 illustrations, 192 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

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:

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

Nyström-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

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.

Other Format:

Printed edition:

ISBN:

978-3-030-10928-8

9783030109288

Access Restriction:

Restricted for use by site license.