Algorithms for clustering data
Algorithms for clustering data
The nature of statistical learning theory
The nature of statistical learning theory
ACM Computing Surveys (CSUR)
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Core Vector Machines: Fast SVM Training on Very Large Data Sets
The Journal of Machine Learning Research
Multiclass core vector machine
Proceedings of the 24th international conference on Machine learning
Simpler core vector machines with enclosing balls
Proceedings of the 24th international conference on Machine learning
Computational Geometry: Theory and Applications
A fast fuzzy clustering algorithm
AIKED'07 Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases - Volume 6
AD-SVMs: A light extension of SVMs for multicategory classification
International Journal of Hybrid Intelligent Systems - Data Mining and Hybrid Intelligent Systems
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
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Minimal Enclosing Ball (MEB) has a limitation for dealing with a large dataset in which computational load drastically increases as training data size becomes large. To handle this problem in huge dataset used for speaker recognition and identification system, we propose two algorithms using Fuzzy C-Mean clustering method. Our method uses divide-and-conquer strategy; trains each decomposed sub-problems to get support vectors and retrains with the support vectors to find a global data description of a whole target class. Our study is experimented on Universal Background Model (UBM) architectures in speech recognition and identification system to eliminate all noise features and reducing time training. For this, the training data, learned by Support Vector Machines (SVMs), is partitioned among several data sources. Computation of such SVMs can be efficiently achieved by finding a core-set for the image of the data.