DocumentCode :
3726531
Title :
Fixed-Size Least Squares Support Vector Machines: Scala Implementation for Large Scale Classification
Author :
Mandar Chandorkar;Raghvendra Mall;Oliver Lauwers;Johan A.K. Suykens;Bart De Moor
Author_Institution :
ESAT-STADIUS, KU Leuven, Leuven, Belgium
fYear :
2015
Firstpage :
522
Lastpage :
528
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 leveraging 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 and evaluate the performance of various kernel based FS-LSSVM models.
Keywords :
"Kernel","Computational modeling","Optimization","Support vector machines","Data models","Tuning","Sparks"
Publisher :
ieee
Conference_Titel :
Computational Intelligence, 2015 IEEE Symposium Series on
Print_ISBN :
978-1-4799-7560-0
Type :
conf
DOI :
10.1109/SSCI.2015.83
Filename :
7376656
Link To Document :
بازگشت