Kernel Methods for Improving Text Search Engines Transductive Inference by Using Support Vector Machines

Jorge Ernesto Espinosa Oviedo

Abstract


This paper is intended to present the implementation and testing methodology of  transductive support vector machines (TSVM) proposed by Joachims et al [1]. Initially it explains the concept offering by the Support Vector Machines as optimal classifiers and clarifies the concept of transductive inference. Along the implementation process several tests were performed. The data used for such tests was very diverse especially with respect to the dimensionality (number of samples, features,etc.). The ultimate objective was to integrate the Transductive inference tool in the already developed Intelligent Interface Web Engine [2] from the SISTA group at the Catholic University of Leuven (Belgium) [2].


Keywords


Support Vector Machines, SVM, Text Classification, Transductive Inference, Text Classification, Data mining.

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References


THORSTEN JOACHIMS – Learning to Classify Text Using Support Vector Machines – Methods, Theory and Algorithms. Kluwer Academic Publishers, Norwel Massachusetts USA 2002.

JANSSENS FRIZO, SPIESSENS THOMAS – Design of an Intelligent Interface – Interfacing a Bibliographic Database. Developed for the KUL - MAI project in 2001-2002

SUYKENS JOHAN A, VAN GESTEL TONY, DE BRABANTER JOS, DEMOOR BART AND VANDEWALLE JOOS – Least Squares Support Vector Machines. World Scientific, Singapore 2002

CRISRTIANINI NELLO SHAWE-TAYLOR JOHN. An introduction to Support Vector Machines and other Kernel –based learning Methods. Cambridge University Press.

SIMON HAYKIN. Neural Networks. A comprehensive foundation. Prentice Hall (London), 1999.

BERNHARD SCHÖLKOPF, SEBASTIAN MIKA, CHRIS J. C. BURGES, PHILIPP KNIRSCH, KLAUS-ROBERT MÜLLER, GUNNAR RÄTSCH, AND ALEXANDER J. SMOLA. Input Space Versus Feature Space in Kernel-Based Methods -IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 10, NO. 5, SEPTEMBER 1999

SVMlight - Support Vector Machines.

Thorsten Joachims - Cornell University - Department of Computer Science

http://svmlight.joachims.org/

LS-SVMlab Toolbox User's Guide - version 1.5

http://www.esat.kuleuven.ac.be/sista/lssvmlab/tutorial/

LOQO Optimisation and Application Website

http://www.orfe.princeton.edu/~loqo/




DOI: http://dx.doi.org/10.18180/tecciencia.2017.22.6

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