Machine Learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Complexity Measures of Supervised Classification Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature subset selection using a new definition of classifiability
Pattern Recognition Letters
Radial Basis Functions
Multiresolution Estimates of Classification Complexity
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data complexity assessment in undersampled classification of high-dimensional biomedical data
Pattern Recognition Letters
Performance analysis of LVQ algorithms: a statistical physics approach
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Data Complexity in Pattern Recognition (Advanced Information and Knowledge Processing)
Integrating support vector machines and neural networks
Neural Networks
An analysis of how training data complexity affects the nearest neighbor classifiers
Pattern Analysis & Applications
Pattern Classifier Design by Linear Programming
IEEE Transactions on Computers
Risk-sensitive loss functions for sparse multi-category classification problems
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A novel kernel-based maximum a posteriori classification method
Neural Networks
Margin calibration in SVM class-imbalanced learning
Neurocomputing
A fast multi-output RBF neural network construction method
Neurocomputing
So near and yet so far: New insight into properties of some well-known classifier paradigms
Information Sciences: an International Journal
Simultaneous feature selection and classification using kernel-penalized support vector machines
Information Sciences: an International Journal
Data characterization for effective prototype selection
IbPRIA'05 Proceedings of the Second Iberian conference on Pattern Recognition and Image Analysis - Volume Part II
Domain of competence of XCS classifier system in complexity measurement space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
Classifiability-based omnivariate decision trees
IEEE Transactions on Neural Networks
Information Sciences: an International Journal
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
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In this work we jointly analyze the performance of three classic Artificial Neural Network models and one Support Vector Machine with respect to a series of data complexity measures known as measures of separability of classes. In particular, we consider a Radial Basis Function Network, a Multi-Layer Perceptron, a Learning Vector Quantization, while the Sequential Minimal Optimization method is used to model the Support Vector Machine. We consider five measures of separability of classes over a wide range of data sets built from real data which have proved to be very discriminative when analyzing the performance of classifiers. We find that two of them allow us to extract common behavior patterns for the four learning methods due to their related nature. We obtain rules using these two metrics that describe both good or bad performance of the Artificial Neural Networks and the Support Vector Machine. With the obtained rules, we characterize the performance of the methods from the data set complexity metrics and therefore their common domains of competence are established. Using these domains of competence the shared good and bad capabilities of these four models can be used to know if the approximative models will perform well or poorly or if a more complex configuration of the model is needed for a given problem in advance.