Decision tree learning algorithms produce accurate models that can be interpreted by domain experts. However, these algorithms are known to be unstable – they can produce drastic...
In learning from examples it is often useful to expand an attribute-vector representation by intermediate concepts. The usual advantage of such structuring of the learning problemi...
Janez Demsar, Blaz Zupan, Marko Bohanec, Ivan Brat...
An important theoretical tool in machine learning is the bias/variance decomposition of the generalization error. It was introduced for the mean square error in [3]. The bias/vari...
Abstract. A hidden Markov model is introduced for descriptive modelling the mosaic–like structures of haplotypes, due to iterated recombinations within a population. Methods usin...
Mikko Koivisto, Teemu Kivioja, Heikki Mannila, Pas...
This paper introduces a new method using dyadic decision trees for estimating a classification or a regression function in a multiclass classification problem. The estimator is bas...