Kernel Based Algorithms For Mining Huge Data Sets
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Kernel Based Algorithms for Mining Huge Data Sets
This is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction and shows the similarities and differences between the two most popular unsupervised techniques.
Web-Based Supply Chain Management and Digital Signal Processing: Methods for Effective Information Administration and Transmission
Presents trends and techniques for successful intelligent decision-making andtransfer of products through digital signal processing.
Handbook of Mathematical Methods in Imaging
Author: Otmar Scherzer
language: en
Publisher: Springer Science & Business Media
Release Date: 2010-11-23
The Handbook of Mathematical Methods in Imaging provides a comprehensive treatment of the mathematical techniques used in imaging science. The material is grouped into two central themes, namely, Inverse Problems (Algorithmic Reconstruction) and Signal and Image Processing. Each section within the themes covers applications (modeling), mathematics, numerical methods (using a case example) and open questions. Written by experts in the area, the presentation is mathematically rigorous. The entries are cross-referenced for easy navigation through connected topics. Available in both print and electronic forms, the handbook is enhanced by more than 150 illustrations and an extended bibliography. It will benefit students, scientists and researchers in applied mathematics. Engineers and computer scientists working in imaging will also find this handbook useful.