<p>Two-dimensional scatter-plots showing training data, classified data, support vectors and decision boundaries for support vector machine classifier using (a) FOS, (b) SGLDM, (c) GLDS, (d) NGTDM.</p
Classification is one of the most important tasks for different application such as text categorizat...
This paper presents an interpretable approach to detecting patterns in scatter plots, which can help...
Conventional approaches to training a supervised image classification aim to fully describe all of t...
<p>Data points marked with an ‘x’ are used for training, while points marked as ‘o’ were used for te...
<p>Two-dimensional data points belonging to two different classes (circles and squares) are shown in...
The theory of the Support Vector Machine (SVM) algorithm is based on statistical learning theory and...
<p>The Proximal Support Vector Machine Classifier: The planes around which points of the sets A+ an...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
International audienceThe power of computation and large memory of computers nowadays offer a great ...
The accuracy of a supervised image classification is a function of the training data used in its gen...
Appropriate training data always play an important role in constructing an efficient classifier to s...
The accuracy of supervised classification is dependent to a large extent on the input training data....
A linear SVM is a discriminant function that attempts to fit a hyperplane that separates the example...
Abstract:- Support vector machines (SVMs) tackle classification and regression problems by non-linea...
The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Su...
Classification is one of the most important tasks for different application such as text categorizat...
This paper presents an interpretable approach to detecting patterns in scatter plots, which can help...
Conventional approaches to training a supervised image classification aim to fully describe all of t...
<p>Data points marked with an ‘x’ are used for training, while points marked as ‘o’ were used for te...
<p>Two-dimensional data points belonging to two different classes (circles and squares) are shown in...
The theory of the Support Vector Machine (SVM) algorithm is based on statistical learning theory and...
<p>The Proximal Support Vector Machine Classifier: The planes around which points of the sets A+ an...
In this chapter we introduce basic concepts and ideas of the Support Vector Machines (SVM). In the f...
International audienceThe power of computation and large memory of computers nowadays offer a great ...
The accuracy of a supervised image classification is a function of the training data used in its gen...
Appropriate training data always play an important role in constructing an efficient classifier to s...
The accuracy of supervised classification is dependent to a large extent on the input training data....
A linear SVM is a discriminant function that attempts to fit a hyperplane that separates the example...
Abstract:- Support vector machines (SVMs) tackle classification and regression problems by non-linea...
The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Su...
Classification is one of the most important tasks for different application such as text categorizat...
This paper presents an interpretable approach to detecting patterns in scatter plots, which can help...
Conventional approaches to training a supervised image classification aim to fully describe all of t...