In this paper, a new algorithm for fingerprint recognition is presented. It is called the Histogram-Partitioning, Median-Filtering Fingerprint Recognition Algorithm (HMFA). The performance of the algorithm is tested through ensemble averaging of the mean square error. It is applied on different fingerprints having various backgrounds, resolutions and dimensions. Initially, a database is formed ...

We study the ensemble statistics of the particle density in a random medium whose mean transport dynamics describes a continuous time random walk. Starting from a Langevin equation for the particle motion in a single disorder realization, we derive evolution equations for the n-point moments of concentration by coarse graining and ensemble averaging the microscale transport problem. The governi...

Ensemble averaging of molecular states is fundamental for the experimental determination of thermodynamic quantities. A special case occurs for single-molecule investigations under equilibrium conditions, for which free energy, entropy and enthalpy at finite temperatures are challenging to determine with ensemble averaging alone. Here we report a method to directly record time-averaged equilibr...

Dropout is a recently introduced algorithm for training neural network by randomly dropping units during training to prevent their co-adaptation. A mathematical analysis of some of the static and dynamic properties of dropout is provided using Bernoulli gating variables, general enough to accommodate dropout on units or connections, and with variable rates. The framework allows a complete analy...

Applying Data Mining (DM) in education is an emerging interdisciplinary research field also known as Educational Data Mining (EDM). Ensemble techniques have been successfully applied in the context of supervised learning to increase the accuracy and stability of prediction. In this paper, we present a hybrid procedure based on ensemble classification and clustering that enables academicians to ...

We describe an algorithm for optimizing time-averaged objective functions that depend on a chaotic state variable. Such problems are ubiquitous in engineering design. They are challenging, because of the sensitive dependence of the state to perturbations in the design. One consequence of this sensitive dependence is that increasing the averaging period, which improves the accuracy of the object...

Neural network ensemble is a learning paradigm where several neural networks are jointly used to solve a problem. In this paper, the relationship between the generalization ability of the neural network ensemble and the correlation of the individual neural networks is analyzed, which reveals that ensembling a selective subset of individual networks is superior to ensembling all the individual n...

Ensemble classification methods that independently construct component models (e.g., bagging) improve accuracy over single models by reducing the error due to variance. Some work has been done to extend ensemble techniques for classification in relational domains by taking relational data characteristics or multiple link types into account during model construction. However, since these approac...

Peripheral objects and their features become indistinct when closely surrounding but nonoverlapping objects are present. Most models suggest that this phenomenon, called crowding, reflects limitations of visual processing, but an intriguing idea is that it may be, in part, adaptive. Specifically, the mechanism generating crowding may simultaneously facilitate ensemble representations of feature...

The possibilities of an a posteriori synchronization of nonsynchronized data records are investigated for the purpose of signal enhancement of a periodic signal. The theoretical background for an effective frequency domain method is given, and the errors of the averaged complex amplitudes are calculated. The time delay between records is identified via maximum likelihood estimation, which is sl...