Index of papers in PLOS Comp. Biol. that mention
  • support vector
Tor D. Wager, Jian Kang, Timothy D. Johnson, Thomas E. Nichols, Ajay B. Satpute, Lisa Feldman Barrett
Introduction
For example, the most common method uses Support Vector Machines to discriminate affective conditions (e.g., depressed patients vs. controls; [19]), and to discriminate 5 emotions, 10 separate maps (5 choose 2) are required for ‘brute-force’ pattern separation.
Supporting Information
Support Vector Machine analyses.
Supporting Information
The Bayesian Spatial Point Process Model classification results are compared against the support vector machine-based classification described here.
The Value of the Generative BSPP Model as a Computational Approach
For example, when using Support Vector Machines to discriminate the five categories, ten separate classifier maps (5-choose-2) are required to predict the category, rather than relying on a single representation of each category and the likelihood that a particular study belongs to it.
support vector is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Ickwon Choi, Amy W. Chung, Todd J. Suscovich, Supachai Rerks-Ngarm, Punnee Pitisuttithum, Sorachai Nitayaphan, Jaranit Kaewkungwal, Robert J. O'Connell, Donald Francis, Merlin L. Robb, Nelson L. Michael, Jerome H. Kim, Galit Alter, Margaret E. Ackerman, Chris Bailey-Kellogg
Supervised learning: Classification
To assess how much this discrimination depends on the classification approach utilized rather than the underlying information content in the data, we employed three different representative classification techniques: penalized logistic regression (a regularized generalized linear model based on Lasso), regularized random forest (a tree-based model), and support vector machine (a kernel-based model).
Supervised learning: Regression
Again, three representative techniques were used to broadly assess the general ability of the data to support predictive models: Lars (regularized linear regression based on Lasso), Gaussian process regression (a nonlinear model), and support vector regression (a ker-nel-based model).
Supervised learning: Regression
As discussed in the methods, the presented results employ a polynomial kernel for Gaussian Process Regression and a radial basis kernel for Support Vector Regression; alternative kernels did not improve the performance.
support vector is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Mei Zhan, Matthew M. Crane, Eugeni V. Entchev, Antonio Caballero, Diana Andrea Fernandes de Abreu, QueeLim Ch’ng, Hang Lu
Abstract
The process flow architecture we present here utilizes standard image processing techniques and the multi-tiered application of classification models such as support vector machines (SVM).
Image Analysis and Computational Tools
To implement discrete classification steps using support vector machines, we use the LIBSVM library, which is freely available for multiple platforms including MATLAB [37].
Results
In this work, we chose to use support vector machines for all classification steps because of its insensitivity to specific conditioning of feature sets and therefore being more robust [34, 37].
support vector is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: