Support vector machine based exploratory projection pursuit optimization for user face identification

2015 ◽  
Author(s):  
Sanaa Ghouzali ◽  
Souad Larabi Marie-Sainte
Keyword(s):  
Support Vector Machine ◽  
Projection Pursuit ◽  
Support Vector ◽  
Face Identification ◽  
Exploratory Projection Pursuit

A Prediction Model for Surface Roughness in Milling Based on Projection Pursuit Regression

2013 ◽  
Vol 312 ◽  
pp. 143-147
Author(s):  
Xue Qin Pan ◽  
Na Zhu ◽  
Yu Cai Dong ◽  
Cui Xiang Liu ◽  
Min Lin ◽  
...  
Keyword(s):  
Surface Roughness ◽  
Support Vector Machine ◽  
Prediction Model ◽  
Projection Pursuit ◽  
Least Square ◽  
Support Vector ◽  
Prediction Errors ◽  
Machined Surface ◽  
Projection Pursuit Regression ◽  
Milling Parameters

A novel prediction model for surface roughness based on Projection Pursuit Regression was proposed in this paper. Based on the new model, the effects of milling parameters on surface roughness in milling can be predicted, and the predicted value of surface roughness in the whole working range can be reached with the limited test data, thus the variation law of quality of machined surface following milling parameters can be obtained. Compared with the least square support vector machine, it can be revealed that on the base of the same samples, the construction speed of this Projection Pursuit Regression is 1~2 higher in order of magnitude than that of the least square support vector machine, while the prediction errors are 40 % of the latter. Thus, the prediction model based on Projection Pursuit Regression can be established fast and be forecasted in high-precision, it is suitable for prediction of surface roughness.

Gradient-based fusion of infrared and visual face images using support vector machine for human face identification

2013 ◽  
Author(s):  
Priya Saha ◽  
Mrinal K. Bhowmik ◽  
Debotosh Bhattacharjee ◽  
Barin K. De ◽  
Mita Nasipuri
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Face Identification ◽  
Human Face ◽  
Face Images ◽  
Gradient Based

A comparative QSAR study of SVM and PPR in the correlation of lithium cation basicities

2009 ◽  
Vol 74 (1) ◽  
pp. 217-241 ◽  
Author(s):  
Alan R. Katritzky ◽  
Yueying Ren ◽  
Svetoslav H. Slavov ◽  
Mati Karelson
Keyword(s):  
Support Vector Machine ◽  
Gas Phase ◽  
Projection Pursuit ◽  
The Other ◽  
Lithium Cation ◽  
Support Vector ◽  
Qsar Study ◽  
Projection Pursuit Regression ◽  
Physico Chemical ◽  
Insight Into

Correlation of gas-phase lithium cation basicities (LCB) of 259 diverse compounds extends the published datasets utilizing multilinear, support vector machine (SVM) and projection pursuit regression (PPR) modeling. The best multiple linear regression (BMLR) method implemented in CODESSA was used to: (i) build multiparameter linear QSPR models and (ii) select set of descriptors for further treatment by the SVM and PPR. The external predictivity and the performance of each of the above methods was estimated and compared to those of the other techniques. The PPR method produced results superior to SVM, which in turn outperformed MLR. The physico-chemical interpretation of each of the descriptors provides new insight into the mechanism of LCB interactions.

scholarly journals Face Identification based Bio-Inspired Algorithms

2019 ◽  
Vol 17 (1) ◽  
pp. 118-127
Author(s):  
Sanaa Ghouzali ◽  
Souad Larabi
Keyword(s):  
Projection Pursuit ◽  
Optimization Methods ◽  
Support Vector ◽  
Biometric Identification ◽  
Face Identification ◽  
Identification System ◽  
Identification Performance ◽  
Face Database ◽  
Swarm Optimization ◽  
Database Size

Most biometric identification applications suffer from the curse of dimensionality as the database size becomes very large, which could negatively affect both the identification performance and speed. In this paper, we use Projection Pursuit (PP) methods to determine clusters of individuals. Support Vector Machine (SVM) classifiers are then applied on each cluster of users separately. PP clustering is conducted using Friedman and Kurtosis projection indices optimized by Genetic Algorithm and Particle Swarm Optimization methods. Experimental results obtained using YALE face database showed improvement in the performance and speed of face identification system

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.

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