Solving the quadratic programming problem arising in support vector classification
Advances in kernel methods
Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Machine Learning
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Support Vector Machine Active Learning with Application sto Text Classification
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Random Sampling Technique for Training Support Vector Machines
ALT '01 Proceedings of the 12th International Conference on Algorithmic Learning Theory
SVMTorch: support vector machines for large-scale regression problems
The Journal of Machine Learning Research
Classifying large data sets using SVMs with hierarchical clusters
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Training ν-Support Vector Classifiers: Theory and Algorithms
Neural Computation
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Advances in Fuzzy Clustering and its Applications
Advances in Fuzzy Clustering and its Applications
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Self-organizing maps with a time-varying structure
ACM Computing Surveys (CSUR)
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Support Vector Machines (SVMs) represent a well known technique for data classification. However, the complexity of the training process makes the SVMs unsuitable for classifying large datasets. Examples of existing approaches to this problem are sampling of the input datasets or clustering of similar inputs. On the other hand, the Growing Neural Gas algorithm (GNG) is a robust tool for cluster analysis, capable of learning the topology of the data. It overcomes most of the common issues of clustering techniques such as predefined number of clusters or beforehand specified cluster radius. This paper presents a solution to the problem of classifying large datasets via learning of the data topology. The described algorithm combines the GNG algorithm with the SVM solver into a specific algorithm for classification of large datasets the GNG-SVM framework. The input dataset is first preprocessed with the GNG algorithm. A new reduced training dataset is created from the extracted topological knowledge. Because the size of the dataset is significantly reduced, the training process of the SVM solver becomes substantially less memory demanding. The performance of the proposed GNGSVM framework is tested on both synthetic and benchmark real world datasets.