Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Kernel Nearest-Neighbor Algorithm
Neural Processing Letters
Composite Kernels for Hypertext Categorisation
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Reducing Communication for Distributed Learning in Neural Networks
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A survey of kernels for structured data
ACM SIGKDD Explorations Newsletter
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
Quadratic kernel-free non-linear support vector machine
Journal of Global Optimization
An algorithm for the recognition of levels of congestion in road traffic problems
Mathematics and Computers in Simulation
Digging into ip flow records with a visual kernel method
CISIS'11 Proceedings of the 4th international conference on Computational intelligence in security for information systems
Enforcing security with behavioral fingerprinting
Proceedings of the 7th International Conference on Network and Services Management
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In a computational context, classification refers to assigning objects to different classes with respect to their features, which can be mapped to qualitative or quantitative variables. Several techniques have been developed recently to map the available information into a set of features (feature space) that improve the classification performance. Kernel functions provide a nonlinear mapping that implicitly transforms the input space to a new feature space where data can be separated, clustered and classified more easily. In this paper a kernel revised version of the Total Recognition by Adaptive Classification Experiments (T.R.A.C.E) algorithm, an iterative k-means like classification algorithm is presented.