Paper Info
Title
On the effectiveness of distributed learning on different class-probability distributions of data
Abstract
The unrestrainable growth of data in many domains in which machine learning could be applied has brought a new field called largescale learning that intends to develop efficient and scalable algorithms with regard to requirements of computation, memory, time and communications. A promising line of research for large-scale learning is distributed learning. It involves learning from data stored at different locations and, eventually, select and combine the "local" classifiers to obtain a unique global answer using one of three main approaches. This paper is concerned with a significant issue that arises when distributed data comes in from several sources, each of which has a different distribution. The class-probability distribution of data (CPDD) is defined and its impact on the performance of the three combination approaches is analyzed. Results show the necessity of taking into account the CPDD, concluding that combining only related knowledge is the most appropriate manner for learning in a distributed manner.
Year
DOI
Venue
2011
10.1007/978-3-642-25274-7_12
CAEPIA
Keywords
Field
DocType
main approach,large-scale learning,different class-probability distribution,different location,combination approach,machine learning,different distribution,largescale learning,new field,class-probability distribution,appropriate manner
Online machine learning,Competitive learning,Instance-based learning,Stability (learning theory),Semi-supervised learning,Active learning (machine learning),Computer science,Unsupervised learning,Artificial intelligence,Computational learning theory,Machine learning
Conference
Volume
ISSN
Citations 
7023.0
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Diego Peteiro-Barral1709.07
Bertha Guijarro-Berdiñas229634.36
Beatriz Pérez-Sánchez39514.03