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. |
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. |
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]. |