Michaelis / Piatkowski / Stolpe Solving Large Scale Learning Tasks. Challenges and Algorithms

Essays Dedicated to Katharina Morik on the Occasion of Her 60th Birthday
1. Auflage 2016
ISBN: 978-3-319-41706-6
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 387 Seiten, eBook

Reihe: Lecture Notes in Artificial Intelligence

In celebration of Prof. Morik's 60th birthday, this Festschrift covers research areas that Prof. Morik worked in and presents various researchers with whom she collaborated.The 23 refereed articles in this Festschrift volume provide challenges and solutions from theoreticians and practitioners on data preprocessing, modeling, learning, and evaluation. Topics include data-mining and machine-learning algorithms, feature selection and feature generation, optimization as well as efficiency of energy and communication.

Zielgruppe

Research

Autoren/Hrsg.

Michaelis, Stefan

Piatkowski, Nico

Stolpe, Marco

Inhaltsverzeichnis

Online Social Networks Event Detection.- Detecting Events in Online Social Networks: Definitions, Trends and Challenges.- Why do we need data privacy.- Sharing Data with Guaranteed Privacy.- Distributed Support Vector Machines.- Big Data Classification — Aspects on Many Features.- Knowledge Discovery from Complex High Dimensional Data.- Local Pattern Detection in Attributed Graphs.- Advances in Exploratory Pattern Analytics on Ubiquitous Data and Social Media.- Understanding Human Mobility with Big Data.- On Event Detection from Spatial Time series for Urban Traffic Applications.- Compressible Reparametrization of Time-Variant Linear Dynamical Systems.- Detection of Local Intensity Changes in Grayscale Images with Robust Methods for Time-Series Analysis.- SCHEP — A Geometric Quality Measure for Regression Rule Sets, Gauging Ranking Consistency Throughout the Real-Valued Target Space.- Bayesian Ordinal Aggregation of Peer Assessments: A Case Study on KDD 2015.- Collaborative on linelearning of an action model.- Ontology-based Classification — Application of Machine Learning Concepts without Learning.- Deep Distant Supervision: Learning Statistical Relational Models for Weak Supervision in Natural Language Extraction.- Supervised Extraction of Usage Patterns in Different Document Representations.- Data-Driven Analyses of Electronic Text Books.- k-Morik: Mining Patterns to Classify Cartified Images of Katharina.