Index of papers in PLOS Comp. Biol. that mention
  • SVM
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 ).
Bright-Field Head Identification
However, in addition to informative feature selection and the curation of a representative training set, the performance of SVM classification models is subject to several parameters associated with the model itself and its kernel function [34, 48].
Bright-Field Head Identification
Thus, to ensure good performance of the final SVM model, we first optimize model parameters based on fivefold cross-validation on the training set (Fig 3A and 3B, Materials and Methods).
Bright-Field Head Identification
Therefore, we optimize the SVM parameters via the minimization of an adjusted error rate that penalizes false negatives more than false positives (Fig 3B).
Discussion
The use of this multivariate information with a classification model such as SVM obviates the need for manually assessing rectilinear thresholds for classification.
Discussion
Moreover, the performance of our classifiers demonstrate that the potentially nonlinear, multidimensional classification provided by SVM prove more powerful than rectilinear thresholding of individual features or dimensionality reduction techniques (Fig 3C and 3F).
Identification of Fluorescently Labeled Cells
Using this feature set, we optimize and train a layer 1 SVM classifier using a manually annotated training set (n = 218) (S4A Fig, Materials and Methods) and show that it is sufficient for identifying cellular regions with relatively high sensitivity and specificity (Fig 5D and 84A Fig).
Identification of Fluorescently Labeled Cells
To construct our layer 2 classifier, we optimize and train an SVM model based on these pairwise relational features (S4B Fig).
Identification of Fluorescently Labeled Cells
In this case, the probability estimates from the SVM classifier [37, 49]
SVM is mentioned in 12 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
The PLR approach was generally superior, with RF quite comparable and SVM somewhat degraded but still yielding good performance.
Supervised learning: Classification
SVM is a kernel-based nonlinear classifier that finds a separating hyperplane (in a space defined by the kernel) between the classes, so as to minimize the risk of classification error.
Supervised learning: Regression
SVR is based on the same theory as SVM , discussed above, but uses the kernel-based approach to fit a regression model to reduce the quantitative prediction error.
Supervised learning: Regression
As with SVM , we evaluated the standard linear, polynomial, and radial basis kernels and presented the results for the radial basis function.
SVM is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Minseung Kim, Violeta Zorraquino, Ilias Tagkopoulos
Parameter settings, implementation, and availability
For the multi-class SVM , one-versus-rest (OVR) approach was used in which for each class, a binary classifier is built for the class label and the rest.
Parameter settings, implementation, and availability
Each binary SVM was built using Gaussian Radial Basis Function (RBF) kernel and the default sigma factor of 1 was used.
Supporting Information
In each iteration, the phase of all samples that were originally unannotated is predicted, based on an ensample of 4 machine learning methods (Naive Bayes, SVM , Decision Tree, KNN) that produce a consensus outcome, as described in the Methods section of the manuscript.
SVM is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: