Algorithms for clustering data
Algorithms for clustering data
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
An evolutionary approach to Transduction in Support Vector Machines
HIS '05 Proceedings of the Fifth International Conference on Hybrid Intelligent Systems
Maximum margin clustering made practical
Proceedings of the 24th international conference on Machine learning
Efficient multiclass maximum margin clustering
Proceedings of the 25th international conference on Machine learning
Support Vector Machines
Unsupervised and semi-supervised multi-class support vector machines
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Subspace maximum margin clustering
Proceedings of the 18th ACM conference on Information and knowledge management
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Acceleration of DBSCAN-based clustering with reduced neighborhood evaluations
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Efficient clustering earth mover's distance
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Speedy local search for semi-supervised regularized least-squares
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Evolutionary kernel density regression
Expert Systems with Applications: An International Journal
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The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding an optimal partition of the data into two classes such that the margin induced by a subsequent application of a support vector machine is maximal. We propose a method based on stochastic search to address this hard optimization problem. While a direct implementation would be infeasible for large data sets, we present an efficient computational shortcut for assessing the "quality" of intermediate solutions. Experimental results show that our approach outperforms existing methods in terms of clustering accuracy.