Add to ORKGCore Vector Machine(CVM) is suitable for efficient large-scale pattern classification. In this paper, a method for improving the performance of CVM with Gaussian kernel function irrespective of the orderings of patterns belonging to different classes within the data set is proposed. This method employs a selective sampling based training of CVM using a novel kernel based scalable hierarchical clustering algorithm. Empirical studies made on synthetic and real world data sets show that the proposed strategy performs well on large data sets
Kernel techniques became popular due to and along with the rising success of Support Vector Machines...
Abstract. In order to handle large-scale pattern classification prob-lems, various sequential and pa...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data set...
This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data set...
Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space....
Classification algorithms have been widely used in many application domains. Most of these domains d...
DoctorIn the last decade, the kernel methods have contributed to significantadvances in research are...
Abstract. Recently, the core vector machine (CVM) has shown significant speedups on classification a...
Support vector machines (SVMs) have been promising methods for classification and regression analysi...
Support vector machines (SVMs) have been promising methods for classification and regression analysi...
This work deals with aspects of support vector machine learning for large-scale data mining tasks. B...
This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The cl...
This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The cl...
Kernel methods, such as the support vector machine (SVM), are often formulated as quadratic programm...
Kernel techniques became popular due to and along with the rising success of Support Vector Machines...
Abstract. In order to handle large-scale pattern classification prob-lems, various sequential and pa...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...
This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data set...
This paper discusses a method for scaling SVM with Gaussian kernel function to handle large data set...
Support Vector Machines(SVMs) are hyperplane classifiers defined in a kernel induced feature space....
Classification algorithms have been widely used in many application domains. Most of these domains d...
DoctorIn the last decade, the kernel methods have contributed to significantadvances in research are...
Abstract. Recently, the core vector machine (CVM) has shown significant speedups on classification a...
Support vector machines (SVMs) have been promising methods for classification and regression analysi...
Support vector machines (SVMs) have been promising methods for classification and regression analysi...
This work deals with aspects of support vector machine learning for large-scale data mining tasks. B...
This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The cl...
This paper proposes a kernel-ensemble bagging SVM classifier for binary class classification. The cl...
Kernel methods, such as the support vector machine (SVM), are often formulated as quadratic programm...
Kernel techniques became popular due to and along with the rising success of Support Vector Machines...
Abstract. In order to handle large-scale pattern classification prob-lems, various sequential and pa...
We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Usi...