Solving Large Scale Learning Tasks. Challenges and Algorithms

Solving Large Scale Learning Tasks. Challenges and Algorithms
Solving Large Scale Learning Tasks. Challenges and Algorithms
Springer | Artificial Intelligence | July 3 2016 | ISBN-10: 3319417053 | 387 pages | pdf | 27.42 mb

Editors: Michaelis, Stefan, Piatkowski, Nico, Stolpe, Marco (Eds.)
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

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.

Number of Illustrations and Tables
73 b/w illustrations
Artificial Intelligence (incl. Robotics)
Information Systems Applications (incl.Internet)
Computer Communication Networks
Algorithm Analysis and Problem Complexity
Data Mining and Knowledge Discovery
Database Management


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