A SUPPORT VECTOR MACHINE FUNCTION APPROXIMATION APPROACH FOR SOLVING THE INVERSE MODELING PROBLEM IN INTELLIGENT ROBOTICS APPLICATIONS

2002 ◽  
Author(s):  
D. A. KARRAS ◽  
Y. S. BOUTALIS ◽  
B. G. MERTZIOS
Keyword(s):  
Support Vector Machine ◽  
Inverse Modeling ◽  
Function Approximation ◽  
Support Vector ◽  
Approximation Approach ◽  
Modeling Problem ◽  
Intelligent Robotics

scholarly journals Incremental and Decremental SVM for Regression

2016 ◽  
Vol 11 (6) ◽  
pp. 755 ◽  
Author(s):  
Honorius Gâlmeanu ◽  
Lucian Mircea Sasu ◽  
Razvan Andonie
Keyword(s):  
Support Vector Machine ◽  
Function Approximation ◽  
Regularization Parameter ◽  
Model Performance ◽  
Regression Function ◽  
Adaptive Resonance Theory ◽  
Support Vector ◽  
Resonance Theory ◽  
Adaptive Resonance ◽  
Regression Methods

Training a support vector machine (SVM) for regression (function approximation) in an incremental/decremental way consists essentially in migrating the input vectors in and out of the support vector set with specific modification of the associated thresholds. We introduce with full details such a method, which allows for defining the exact increments or decrements associated with the thresholds before vector migrations take place. Two delicate issues are especially addressed: the variation of the regularization parameter (for tuning the model performance) and the extreme situations where the support vector set becomes empty. We experimentally compare our method with several regression methods: the multilayer perceptron, two standard SVM implementations, and two models based on adaptive resonance theory.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference
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