ICA (Independent Component Analysis) is a new technique for analyzing multi-variant data. Lots of results are reported in the eld of neurobiological data analysis such as EEG (Electroencephalography), MRI (Magnetic Resonance Imaging), and MEG (Magnetoencephalography) using ICA. But there still remain problems. In most of the neurobiological data, there are a large amount of noise, and the numbe...

This dissertation presents multi-objective multi-task learning, a new learning framework. Given a fixed sequence of tasks, the learned hypothesis space must minimize multiple objectives. Since these objectives are often in conflict, we cannot find a single best solution, so we analyze a set of solutions. We first propose and analyze a new learning principle, empirically efficient learning. From...

Heuristic optimizers are an important tool in academia and industry, and their performance-optimizing configuration requires a significant amount of expertise. As the proper configuration of algorithms is a crucial aspect in the engineering of heuristic algorithms, a significant research effort has been dedicated over the last years towards moving this step to the computer and, thus, make it au...

Resource allocation is the optimal distribution in a limited number of resources available for certain activities. The large activities requires exponentially multiplying computation cost. Therefore, resource problem known as NP-Hard literature. In this study, multi-objective binary artificial bee colony algorithm has been proposed solving problems. benefited from robust structure and easy impl...

The article presents the hybrid metaheuristic-neural assessment of the pull-off adhesion in existing multi-layer cement composites using artificial neural networks (ANNs) and the imperialist competitive algorithm (ICA). The ICA is a metaheuristic algorithm inspired by the human political-social evolution. This method is based solely on the use of ANNs and two non-destructive testing (NDT) metho...

We present a new method for the blind separation of sources, which do not fulfill the independence assumption. In contrast to standard methods we consider groups of neighboring samples ("patches") within the observed mixtures. First we extract independent features from the observed patches. It turns out that the average dependencies between these features in different sources is in general lowe...

Recent advances in the multi-omics characterization necessitate pathway-level abstraction and knowledge integration across different data types. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biol...

In the blind equalization of multi-input multi-output (MIMO) finite impulse response communication channels, co-channel interference (CCI) is typically cancelled by exploiting the properties of digital modulations, such as their finite alphabet (FA). This contribution takes advantage of the mutual independence of the users’ signals through the application of independent component analysis (ICA)...