ONTOLOGIES BASED APPROACH FOR SEMANTIC INDEXING IN DISTRIBUTED ENVIRONMENTS

2009 ◽  
Keyword(s):  
Distributed Environments ◽  
Semantic Indexing

SOAF: Semantic Indexing System Based on Collaborative Tagging

10.28945/371 ◽  
2008 ◽  
Vol 4 ◽  
pp. 137-149 ◽  
Author(s):  
Doina Ana Cernea ◽  
Esther Del Moral-Pérez ◽  
Jose E. Labra Gayo
Keyword(s):  
Semantic Indexing ◽  
Collaborative Tagging ◽  
Indexing System

scholarly journals Evaluating Worksharing Tasks on Distributed Environments

2020 ◽  
Author(s):  
Marcos Maronas ◽  
Xavier Teruel ◽  
J. Mark Bull ◽  
Eduard Ayguade ◽  
Vicenc Beltran
Keyword(s):  
Distributed Environments

Analyzing Large-Scale Proteomics Projects with Latent Semantic Indexing

2008 ◽  
Vol 7 (1) ◽  
pp. 182-191 ◽  
Author(s):  
Sebastian Klie ◽  
Lennart Martens ◽  
Juan Antonio Vizcaíno ◽  
Richard Côté ◽  
Phil Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Latent Semantic Indexing ◽  
Semantic Indexing

SSC: cloud-based data stream management in distributed environments

2016 ◽  
Vol 9 (3) ◽  
pp. 171
Author(s):  
Kok Leong Ong ◽  
Andrzej Goscinski ◽  
Yuzhang Han ◽  
Peter Brezany ◽  
Zahir Tari ◽  
...  
Keyword(s):  
Data Stream ◽  
Distributed Environments ◽  
Data Stream Management ◽  
Stream Management

A Method Based on Support Vector Machine for Feature Selection of Latent Semantic Features

2011 ◽  
Vol 181-182 ◽  
pp. 830-835
Author(s):  
Min Song Li
Keyword(s):  
Support Vector Machine ◽  
Text Categorization ◽  
Latent Semantic Indexing ◽  
Classification Performance ◽  
Compact Representation ◽  
Support Vector ◽  
Semantic Features ◽  
Semantic Indexing ◽  
Feature Extraction Method ◽  
Feature Subspace

Latent Semantic Indexing(LSI) is an effective feature extraction method which can capture the underlying latent semantic structure between words in documents. However, it is probably not the most appropriate for text categorization to use the method to select feature subspace, since the method orders extracted features according to their variance,not the classification power. We proposed a method based on support vector machine to extract features and select a Latent Semantic Indexing that be suited for classification. Experimental results indicate that the method improves classification performance with more compact representation.

scholarly journals Compression Domain Volume Rendering for Distributed Environments

1997 ◽  
Vol 16 (3) ◽  
pp. C95-C107 ◽  
Author(s):  
L. Lippert ◽  
M.H. Gross ◽  
C. Kurmann
Keyword(s):  
Volume Rendering ◽  
Distributed Environments
Sign in / Sign up

Export Citation Format

Share Document