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.

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.

scholarly journals Grasp to See—Object Classification Using Flexion Glove with Support Vector Machine

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1461
Author(s):  
Shun-Hsin Yu ◽  
Jen-Shuo Chang ◽  
Chia-Hung Dylan Tsai
Keyword(s):  
Support Vector Machine ◽  
Daily Life ◽  
Object Classification ◽  
Target Object ◽  
The Other ◽  
Support Vector ◽  
Wide Range ◽  
Dark Space ◽  
Flex Sensors ◽  
Shape And Size

This paper proposes an object classification method using a flexion glove and machine learning. The classification is performed based on the information obtained from a single grasp on a target object. The flexion glove is developed with five flex sensors mounted on five finger sleeves, and is used for measuring the flexion of individual fingers while grasping an object. Flexion signals are divided into three phases, and they are the phases of picking, holding and releasing, respectively. Grasping features are extracted from the phase of holding for training the support vector machine. Two sets of objects are prepared for the classification test. One is printed-object set and the other is daily-life object set. The printed-object set is for investigating the patterns of grasping with specified shape and size, while the daily-life object set includes nine objects randomly chosen from daily life for demonstrating that the proposed method can be used to identify a wide range of objects. According to the results, the accuracy of the classifications are achieved 95.56% and 88.89% for the sets of printed objects and daily-life objects, respectively. A flexion glove which can perform object classification is successfully developed in this work and is aimed at potential grasp-to-see applications, such as visual impairment aid and recognition in dark space.

Sentiment Classification

2015 ◽  
pp. 1-26
Author(s):  
Jalel Akaichi
Keyword(s):  
Support Vector Machine ◽  
Text Mining ◽  
Sentiment Analysis ◽  
Training Model ◽  
Sentiment Classification ◽  
The Other ◽  
Support Vector ◽  
Analysis Techniques ◽  
Sentiment Lexicon ◽  
And Behavior

In this work, we focus on the application of text mining and sentiment analysis techniques for analyzing Tunisian users' statuses updates on Facebook. We aim to extract useful information, about their sentiment and behavior, especially during the “Arabic spring” era. To achieve this task, we describe a method for sentiment analysis using Support Vector Machine and Naïve Bayes algorithms, and applying a combination of more than two features. The output of this work consists, on one hand, on the construction of a sentiment lexicon based on the Emoticons and Acronyms' lexicons that we developed based on the extracted statuses updates; and on the other hand, it consists on the realization of detailed comparative experiments between the above algorithms by creating a training model for sentiment classification.

QSAR Study of Polychlorinated Dibenzodioxins, Dibenzofurans, and Biphenyls using the Heuristic Method and Support Vector Machine

2006 ◽  
Vol 25 (1) ◽  
pp. 46-55 ◽  
Author(s):  
F. Luan ◽  
W. P. Ma ◽  
X. Y. Zhang ◽  
H. X. Zhang ◽  
M. C. Liu ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Heuristic Method ◽  
Support Vector ◽  
Qsar Study ◽  
Polychlorinated Dibenzodioxins
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