We present a method of nominally piecewise multiple regression using a four-layer perceptron to fit multivariate data containing numerical and nominal variables. In our method, each linear regression function is accompanied with the corresponding nominal condition stating a subspace where the function is applied. Our method selects the optimal numbers of hidden units and rules very fast based on the Bayesian Information Criterion (BIC). The proposed method worked well in our experiments using an artificial and two real data sets.
['Yusuke Tanahashi', 'Daisuke Kitakoshi', 'Ryohei Nakano']
Nominally piecewise multiple regression using a four-layer perceptron