作者: Vanya Van Belle , Paulo Lisboa
DOI: 10.1109/CIDM.2013.6597213
关键词: Predictive modelling 、 Radial basis function kernel 、 Kernel method 、 Mathematical model 、 Artificial intelligence 、 Polynomial kernel 、 Support vector machine 、 Selection (genetic algorithm) 、 Computer science 、 Machine learning 、 Data mining 、 Interpretation (logic)
摘要: Support vector machines are highly flexible and generalizing mathematical models that can be used to build prediction models. Their success on a field is not followed by their application in practice due black-box nature. The RBF kernel often but the good performance cannot accompanied an interpretation of results. We present method visualize different components propose select relevant ones. proposed able automatically detect important main two-way interaction effects while still obtaining interpretable illustrated large dataset predict viability pregnancies at end first trimester based initial scan findings.