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Ramblings of a rogue Mathematician

Masters Thesis: ESAT, KU Leuven

Fixed Size Least Squares Support Vector Machines: A Scala based programming framework for Large Scale Classification

Abstract

We propose FS-Scala, a flexible and modular Scala based implementation of the Fixed Size Least Squares Support Vector Machine (FS-LSSVM) for large data sets. The framework consists of a set of modules for (gradient and gradient free) optimization, model representation, kernel functions and evaluation of FS-LSSVM models.

A kernel based Fixed-Size Least Squares Support Vector Machine (FS-LSSVM) model is implemented in the proposed framework, while heavily employing distributed MapReduce via the parallel computing capabilities of Apache Spark. Global optimization routines like Coupled Simulated Annealing (CSA) and Grid Search are implemented and used to tune the hyper-parameters of the FS-LSSVM model.

Finally, we carry out experiments on benchmark data sets like Forest Cover Type, Magic Gamma and Adult, recording the performance and tuning time of various kernel based FS-LSSVM models.

FS-LSSVM: Formulation

The solution of which is given by:

Citation

@mastersthesis{
    author = {Chandorkar, M. H.},
    title = {Fixed Size Least Squares Support Vector Machines:
	A Scala based programming framework for Large Scale Classification},
    school = {Katholieke Universitiet Leuven},
    year = {2015}
}

FS-Scala can be found here.