In this paper we introduce a supervised linear dimensionality reduction algorithm which finds a projected input space that maximizes the mutual information between input and output values. The algorithm utilizes the recently introduced MeanNN estimator for differential entropy. We show that the estimator is an appropriate tool for the dimensionality reduction task. Next we provide a nonlinear r...

Partial evaluation is an automatic optimization of the execution of an application. For some of the components of partial evaluation, graph reduction long has promised practical implementations. The promises have not been fulfilled, since graph reduction itself lacked a practical implementation. Practical graph reduction recently has been achieved. It may satisfy some of the promises of graph r...

The detection of thermal insulation failures in buildings in operation responds to the challenge of improving building energy efficiency. This multidisciplinary study presents a novel four-step soft computing knowledge identification model called IKBIS to perform thermal insulation failure detection. It proposes the use of Exploratory Projection Pursuit methods to study the relation between inp...

We discuss the relation of a certain type of generalized Lyapunov equations to Gramians of stochastic and bilinear systems together with the corresponding energy functionals. While Gramians and energy functionals of stochastic linear systems show a strong correspondence to the analogous objects for deterministic linear systems, the relation of Gramians and energy functionals for bilinear system...

The paper gives a soundness and completeness proof for the implicative fragment of intuitionistic calculus with respect to the semantics of computability logic, which understands intuitionistic implication as interactive algorithmic reduction. This concept — more precisely, the associated concept of reducibility — is a generalization of Turing reducibility from the traditional, input/output sor...

We consider the task of dimensionality reduction for regression (DRR) informed by realvalued multivariate labels. The problem is often treated as a regression task where the goal is to find a low dimensional representation of the input data that preserves the statistical correlation with the targets. Recently, Covariance Operator Inverse Regression (COIR) was proposed as an effective solution t...

Recently deep neural networks based on tanh activation function have shown their impressive power in image denoising. However, much training time is needed because of their very large size. In this letter, we propose a dual-pathway rectifier neural network by combining two rectifier neurons with reversed input and output weights in the same hidden layer. We drive the equivalent activation funct...

This paper proposes a novel type of random forests called a denoising random forests that are robust against noises contained in test samples. Such noise-corrupted samples cause serious damage to the estimation performances of random forests, since unexpected child nodes are often selected and the leaf nodes that the input sample reaches are sometimes far from those for a clean sample. Our main...

A common problem in nonlinear control is the need to consider systems of high complexity. Here we consider systems, which although may be low order, have high complexity due to a complex right hand side of a differential equation (e.g. a right hand side which has many terms – such systems arise from coordinate transformations in constructive nonlinear control designs). This contribution develop...