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Solving Large Scale Learning Tasks. Challenges and Algorithms de Stefan Michaelis

Solving Large Scale Learning Tasks. Challenges and Algorithms: Lecture Notes in Computer Science, cartea 9580

Editat de Stefan Michaelis, Nico Piatkowski, Marco Stolpe
en Limba Engleză Paperback – 3 iul 2016
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.
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Specificații

ISBN-13: 9783319417059
ISBN-10: 3319417053
Pagini: 404
Ilustrații: XIV, 387 p. 73 illus.
Dimensiuni: 155 x 235 x 22 mm
Greutate: 0.61 kg
Ediția:1st edition 2016
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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.

Caracteristici

Contains refereed papers dedicated to Katharina Morik and to her work Collects a number of papers by Prof. Morik's friends and collaborators over the years presenting a broad range of topics reflecting her versatility Adresses a large diversity of topics starting with natural language processing; machine learning, ranging from inductive logic programming to statistical learning; analysis of very large data collections; high-dimensional data; and resource awareness. Latest results include spatio-temporal random fields and integer Markov random fields, both allowing for complex probabilistic graphical models under resource constraints. Includes supplementary material: sn.pub/extras