Support Vector Machine Model for Currency Crisis Discrimination

03/03/2014
by   Arindam Chaudhuri, et al.
0

Support Vector Machine (SVM) is powerful classification technique based on the idea of structural risk minimization. Use of kernel function enables curse of dimensionality to be addressed. However, proper kernel function for certain problem is dependent on specific dataset and as such there is no good method on choice of kernel function. In this paper, SVM is used to build empirical models of currency crisis in Argentina. An estimation technique is developed by training model on real life data set which provides reasonably accurate model outputs and helps policy makers to identify situations in which currency crisis may happen. The third and fourth order polynomial kernel is generally best choice to achieve high generalization of classifier performance. SVM has high level of maturity with algorithms that are simple, easy to implement, tolerates curse of dimensionality and good empirical performance. The satisfactory results show that currency crisis situation is properly emulated using only small fraction of database and could be used as an evaluation tool as well as an early warning system. To the best of knowledge this is the first work on SVM approach for currency crisis evaluation of Argentina.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
10/16/2022

A new trigonometric kernel function for support vector machine

In the last few years, various types of machine learning algorithms, suc...
research
07/22/2015

Practical Selection of SVM Supervised Parameters with Different Feature Representations for Vowel Recognition

It is known that the classification performance of Support Vector Machin...
research
07/14/2021

Efficient Learning of Pinball TWSVM using Privileged Information and its applications

In any learning framework, an expert knowledge always plays a crucial ro...
research
11/06/2015

Neutralized Empirical Risk Minimization with Generalization Neutrality Bound

Currently, machine learning plays an important role in the lives and ind...
research
09/10/2022

Software Defect Prediction Using Support Vector Machine

Software defect prediction is an essential task during the software deve...
research
11/21/2019

Random Machines: A bagged-weighted support vector model with free kernel choice

Improvement of statistical learning models in order to increase efficien...
research
12/31/2016

Very Fast Kernel SVM under Budget Constraints

In this paper we propose a fast online Kernel SVM algorithm under tight ...

Please sign up or login with your details

Forgot password? Click here to reset