The angles and lengths of the yellow lines in each cell represent direction and strength of decline in an QPM image. (a) Weight vectors of QPM images (OPL) normalized by path length. Left side represents visualized weight vectors of HOG feature of cell lines. Right side represents vectors of WBCs. (b) Weight vectors of QPM images (OPL) normalized by diameter. In each panel, the left sides represent weight vectors of HOG feature of cell lines. The right sides represent the vectors of WBCs.</p
The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architectur...
SVM classification accuracy in %, with classification based on smoothed (unsmoothed) data within the...
Binary images of MDA-MB-231 GFP cells in 3D Collagen-I matrices. Cells manually classified based on ...
(a) QPM image of cell line. (b) QPM image of WBC. In (a) and (b), pseudo color represents OPL. OPL i...
<p>Column weights obtained from SVM rank values (a), and (b). These are the total weight percentages...
<p>The red and blue spots indicate the spatial distribution of cells in the numeric feature space. T...
A linear SVM is a discriminant function that attempts to fit a hyperplane that separates the example...
<p>SVM Classification Results for Linear Kernel for test v/s all five backgrounds (Variance Reduced)...
Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...
After obtaining the T1- and T2-weighted images (a-b) from their corresponding sequences, the body re...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
<p>The bottom row shows all data points for the same individuals with the difference that the class ...
<p>The classification results of linear, polynomial, RBF and sigmoid SVMs with the optimal parameter...
Abstract. For better interpretability of class structure in data we want to use Support Vector Machi...
<p><b>Copyright information:</b></p><p>Taken from "SVM-Fold: a tool for discriminative multi-class p...
The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architectur...
SVM classification accuracy in %, with classification based on smoothed (unsmoothed) data within the...
Binary images of MDA-MB-231 GFP cells in 3D Collagen-I matrices. Cells manually classified based on ...
(a) QPM image of cell line. (b) QPM image of WBC. In (a) and (b), pseudo color represents OPL. OPL i...
<p>Column weights obtained from SVM rank values (a), and (b). These are the total weight percentages...
<p>The red and blue spots indicate the spatial distribution of cells in the numeric feature space. T...
A linear SVM is a discriminant function that attempts to fit a hyperplane that separates the example...
<p>SVM Classification Results for Linear Kernel for test v/s all five backgrounds (Variance Reduced)...
Ranked accuracies from the support vector machine analysis described in the paper for all of the cla...
After obtaining the T1- and T2-weighted images (a-b) from their corresponding sequences, the body re...
Support vector machines (SVMs) are a family of machine learning methods, originally introduced for t...
<p>The bottom row shows all data points for the same individuals with the difference that the class ...
<p>The classification results of linear, polynomial, RBF and sigmoid SVMs with the optimal parameter...
Abstract. For better interpretability of class structure in data we want to use Support Vector Machi...
<p><b>Copyright information:</b></p><p>Taken from "SVM-Fold: a tool for discriminative multi-class p...
The Support Vector Machine (SVM) is a new type of learning machine. The SVM is a general architectur...
SVM classification accuracy in %, with classification based on smoothed (unsmoothed) data within the...
Binary images of MDA-MB-231 GFP cells in 3D Collagen-I matrices. Cells manually classified based on ...