Add to ORKGAbstract. In the simplest form support vector machines (SVM) de-fine a separating hyperplane between classes generated from a subset of cases, called support vectors. The support vectors “mark ” the boundary between two classes. The result is an interpretable classifier, where the importance of the variables to the classification, is identified by the co-efficients of the variables defining the hyperplane. This paper describes visual methods that can be used with classifiers to understand cluster structure in data. The study leads to suggestions for adaptations to the SVM algorithm and ways for other classifiers to borrow from the SVM approach to improve the result. The end result for any data problem is a classification rule that is easy t...
Support vector machines (SVMs) have been widely adopted for classification, regression and novelty d...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
<p>Although, SVMs work in multidimensional space where the dimensionality depends on the number voxe...
In the simplest form support vector machines (SVM) define a separating hyperplane between classes ge...
In this note, we propose a novel classification approach by introducing a new clustering method, whi...
Abstract. For better interpretability of class structure in data we want to use Support Vector Machi...
International audienceThe power of computation and large memory of computers nowadays offer a great ...
The visualization of support vector machines in realistic settings is a difficult problem due to the...
We present a novel method for clustering using the support vector machine approach. Data points are ...
In this paper, some ways of visualization of support vectors are investigated. We examine a possibil...
We present a novel clustering method using the approach of support vector machines. Data points are...
Classifier is an important element for a classification process to classify features or samples of a...
We present a novel algorithm for image classification that is targeted to capture class variability....
Shaded similarity matrix has long been used in visual cluster analysis. This paper investigates how ...
In this work, we provide an exposition of the support vector machine classifier (SVMC) algorithm. We...
Support vector machines (SVMs) have been widely adopted for classification, regression and novelty d...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
<p>Although, SVMs work in multidimensional space where the dimensionality depends on the number voxe...
In the simplest form support vector machines (SVM) define a separating hyperplane between classes ge...
In this note, we propose a novel classification approach by introducing a new clustering method, whi...
Abstract. For better interpretability of class structure in data we want to use Support Vector Machi...
International audienceThe power of computation and large memory of computers nowadays offer a great ...
The visualization of support vector machines in realistic settings is a difficult problem due to the...
We present a novel method for clustering using the support vector machine approach. Data points are ...
In this paper, some ways of visualization of support vectors are investigated. We examine a possibil...
We present a novel clustering method using the approach of support vector machines. Data points are...
Classifier is an important element for a classification process to classify features or samples of a...
We present a novel algorithm for image classification that is targeted to capture class variability....
Shaded similarity matrix has long been used in visual cluster analysis. This paper investigates how ...
In this work, we provide an exposition of the support vector machine classifier (SVMC) algorithm. We...
Support vector machines (SVMs) have been widely adopted for classification, regression and novelty d...
Cluster analysis methods are used to classify R unlabeled objects in a P-dimensional space into grou...
<p>Although, SVMs work in multidimensional space where the dimensionality depends on the number voxe...