This paper shows that the normalized maximum likelihood (NML) code-length calculated in [1] is an upper bound on the NML code-length strictly calculated for the Gaussian Mixture Model. We call an upper bound on the NML code-length as uNML (upper bound on NML). When we use this uNML code-length, we have to change the scale of data sequence to satisfy the restricted domain. However, in the point ...

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this letter, we consider a feature-wise kernelized Lasso for capturing nonlinear inp...

The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear inp...

This work presents a black-box input selection approach to reveal causal dependencies between process variables of complex industrial systems. This allows data based modeling with physically interpretable model structure. For this purpose a method is used which combines statistical and analytical approaches to find causal relations between measured data, detection of control loops and the inter...

Data driven variable selection, without including physical knowledge, is an important prerequisite for many applications in the field of data based modeling. This paper deals with a novel approach to optimize the dimension of the input space by a combination of common variable selection methods with multivariate correlation analysis. The results are input structures with revised pseudo correlat...

A prediction method of protein disulfide bond based on support vector machine and sample selection is proposed in this paper. First, the protein sequences selected are encoded according to a certain encoding, input data for the prediction model of protein disulfide bond is generated; Then sample selection technique is used to select a portion of input data as training samples of support vector ...

Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and have been studied extensively. However, these two issues seem to have been investigated separately as two independent problems. If training input points and models are simultaneously optimized, the generalization perfor...