Holmes, Dawn E.

Data Mining: Foundations and Intelligent Paradigms Volume 2: Statistical, Bayesian, Time Series and other Theoretical Aspects / [electronic resource] : edited by Dawn E. Holmes, Lakhmi C. Jain. - Berlin, Heidelberg : Springer Berlin Heidelberg, 2012. - XIV, 250 p. online resource. - Intelligent Systems Reference Library, 24 1868-4394 ; . - Intelligent Systems Reference Library, 24 .

From the content: Data Mining with Multilayer Perceptrons and Support Vector Machines -- Regulatory Networks under Ellipsoidal Uncertainty - Data Analysis and Prediction by Optimization Theory and Dynamical Systems -- A Visual Environment for Designing and Running Data Mining Workflows in the Knowledge Grid -- Formal framework for the Study of Algorithmic Properties of Objective Interestingness Measures -- Nonnegative Matrix Factorization: Models, Algorithms and Applications -- Visual Data Mining and Discovery with Binarized Vectors.

Data mining is one of the most rapidly growing research areas in computer science and statistics. In Volume 2 of this three volume series, we have brought together contributions from some of the most prestigious researchers in theoretical data mining. Each of the chapters is self contained. Statisticians and applied scientists/ engineers will find this volume valuable. Additionally, it provides a sourcebook for graduate students interested in the current direction of research in data mining.


10.1007/978-3-642-23241-1 doi

Medical records--Data processing.
Artificial intelligence.
Computational Intelligence.
Artificial Intelligence (incl. Robotics).
Health Informatics.



Implemented and Maintained by Biju Patnaik Central Library.
For any Suggestions/Query Contact to library or Email: library@nitrkl.ac.in OR bpcl-cir@nitrkl.ac.in. Ph:91+6612462103
Website/OPAC best viewed in Mozilla Browser in 1366X768 Resolution.

Powered by Koha