Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Machine Learning
Using the Fisher Kernel Method to Detect Remote Protein Homologies
Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology
Text classification using string kernels
The Journal of Machine Learning Research
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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We present a method, called equivalence learning, which applies a two-class classification approach to object-pairs defined within a multi-class scenario. The underlying idea is that instead of classifying objects into their respective classes, we classify object pairs either as equivalent (belonging to the same class) or non-equivalent (belonging to different classes). The method is based on a vectorisation of the similarity between the objects and the application of a machine learning algorithm (SVM, ANN, LogReg, Random Forests) to learn the differences between equivalent and non-equivalent object pairs, and define a unique kernel function that can be obtained via equivalence learning. Using a small dataset of archaeal, bacterial and eukaryotic 3-phosphoglycerate-kinase sequences we found that the classification performance of equivalence learning slightly exceeds those of several simple machine learning algorithms at the price of a minimal increase in time and space requirements.