DocumentCode :
1013195
Title :
Distributed support vector machines
Author :
Navia-Vazquez, A. ; Parrado-Hernandez, Emilio
Author_Institution :
Dept. of Signal Theor. & Commun., Univ. Carlos III de Madrid, Spain
Volume :
17
Issue :
4
fYear :
2006
fDate :
7/1/2006 12:00:00 AM
Firstpage :
1091
Lastpage :
1097
Abstract :
A truly distributed (as opposed to parallelized) support vector machine (SVM) algorithm is presented. Training data are assumed to come from the same distribution and are locally stored in a number of different locations with processing capabilities (nodes). In several examples, it has been found that a reasonably small amount of information is interchanged among nodes to obtain an SVM solution, which is better than that obtained when classifiers are trained only with the local data and comparable (although a little bit worse) to that of the centralized approach (obtained when all the training data are available at the same place). We propose and analyze two distributed schemes: a "naïve" distributed chunking approach, where raw data (support vectors) are communicated, and the more elaborated distributed semiparametric SVM, which aims at further reducing the total amount of information passed between nodes while providing a privacy-preserving mechanism for information sharing. We show the feasibility of our proposal by evaluating the performance of the algorithms in benchmarks with both synthetic and real-world datasets.
Keywords :
learning (artificial intelligence); support vector machines; distributed semiparametric SVM; distributed support vector machines; information sharing; naive distributed chunking approach; privacy-preserving mechanism; training data; Computer networks; Data mining; Data privacy; IP networks; Image databases; Information analysis; Proposals; Support vector machine classification; Support vector machines; Training data; Collaborative; compact; coopetitive; data mining; data privacy; distributed; information sharing; support vector machine;
fLanguage :
English
Journal_Title :
Neural Networks, IEEE Transactions on
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/TNN.2006.875968
Filename :
1650264
Link To Document :
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