Support Vector Machines and Affective Science

2020 ◽  
Vol 12 (4) ◽  
pp. 297-308
Author(s):  
Chris H. Miller ◽  
Matthew D. Sacchet ◽  
Ian H. Gotlib
Keyword(s):  
Support Vector Machines ◽  
Reduction Technique ◽  
Feature Reduction ◽  
Data Driven ◽  
Future Research ◽  
Support Vector ◽  
Predictive Algorithm ◽  
Affective Science ◽  
Vector Machines ◽  
Potential Applications

Support vector machines (SVMs) are being used increasingly in affective science as a data-driven classification method and feature reduction technique. Whereas traditional statistical methods typically compare group averages on selected variables, SVMs use a predictive algorithm to learn multivariate patterns that optimally discriminate between groups. In this review, we provide a framework for understanding the methods of SVM-based analyses and summarize the findings of seminal studies that use SVMs for classification or data reduction in the behavioral and neural study of emotion and affective disorders. We conclude by discussing promising directions and potential applications of SVMs in future research in affective science.

scholarly journals Assessment of rainfall aggregation and disaggregation using data-driven models and wavelet decomposition

2016 ◽  
Vol 48 (1) ◽  
pp. 99-116 ◽  
Author(s):  
Sungwon Kim ◽  
Ozgur Kisi ◽  
Youngmin Seo ◽  
Vijay P. Singh ◽  
Chang-Joon Lee
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Wavelet Decomposition ◽  
Data Driven ◽  
Support Vector ◽  
Generalized Regression Neural Networks ◽  
Vector Machines ◽  
Aggregation And Disaggregation ◽  
Generalized Regression ◽  
Mother Wavelets

The objective of this study is to develop hybrid models by combining data-driven models, including support vector machines (SVM) and generalized regression neural networks (GRNN), and wavelet decomposition for aggregation and disaggregation of rainfall. The wavelet-based support vector machines (WSVM) and wavelet-based generalized regression neural networks (WGRNN) models are obtained using mother wavelets, including db8, db10, sym8, sym10, coif6, and coif12. The developed models are evaluated in the Bocheong-stream catchment, an International Hydrological Program representative catchment, Republic of Korea. WSVM and WGRNN models with mother wavelet db10 yield the best performance as compared with other mother wavelets for estimating areal and disaggregated rainfalls, respectively. Among 12 rainfall stations, SVM, GRNN, WSVM (db10 and sym10), and WGRNN (db10 and sym10) models provide the best accuracies for estimating the disaggregated rainfalls at Samga (No. 7), and the worst accuracies for estimating the disaggregated rainfalls at Yiweon (No. 11) stations, respectively. Results obtained from this study indicate that the combination of data-driven models and wavelet decomposition can be a useful tool for estimating areal and disaggregated rainfalls satisfactorily, and can yield better efficiency than data-driven models.

Feature Reduction for Computationally Efficient Damage State Classification Using Binary Tree Support Vector Machines

2008 ◽  
Author(s):  
Clyde Coelho ◽  
Aditi Chattopadhyay
Keyword(s):  
Support Vector Machines ◽  
Binary Tree ◽  
Feature Reduction ◽  
Training Data ◽  
Support Vector ◽  
Computationally Efficient ◽  
Damage State ◽  
Data Set ◽  
Linear Discriminant ◽  
Vector Machines

This paper proposes a computationally efficient methodology for classifying damage in structural hotspots. Data collected from a sensor instrumented lug joint subjected to fatigue loading was preprocessed using a linear discriminant analysis (LDA) to extract features that are relevant for classification and reduce the dimensionality of the data. The data is then reduced in the feature space by analyzing the structure of the mapped clusters and removing the data points that do not affect the construction of interclass separating hyperplanes. The reduced data set is used to train a support vector machines (SVM) based classifier and the results of the classification problem are compared to those when the entire data set is used for training. To further improve the efficiency of the classification scheme, the SVM classifiers are arranged in a binary tree format to reduce the number of comparisons that are necessary. The experimental results show that the data reduction does not reduce the ability of the classifier to distinguish between classes while providing a nearly fourfold decrease in the amount of training data processed.

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