This study presented a new diagnosis system for myocardial infarction classification by
converting multi-lead ECG data into a density model for increasing accuracy and flexibility of …

Purpose In this preliminary study, recently developed unsupervised nonlinear dimension
reduction (DR) and data representation techniques were applied to computer‐extracted …

Echo State Networks (ESNs) have been shown to be effective for a number of tasks,
including motor control, dynamic time series prediction, and memorizing musical sequences …

With the advances in computer technology, the amount of data that is obtained from various
sources and stored in electronic media is growing at exponential rates. Data mining is a …

This paper presents an impact assessment for the imputation of missing data. The
assessment is performed by measuring the impacts of missing data on the statistical nature …

We describe the use of the nonparametric bootstrap to investigate the accuracy of current
dipole localization from magnetoencephalography (MEG) studies of event-related neural …

Purpose: Unlabeled medical image data are abundant, yet the process of converting them
into a labeled (“truth‐known”) database is time and resource expensive and fraught with …

For measuring three components of velocity in unknown flow fields, multi-hole pressure
probes possess a significant advantage. Unlike methods such as hot-wire anemometry …

This paper is a progress report on a project aimed at the realization of a low-cost, automatic,
trainable system “AutoStage” for recognition and counting of pollen. Previous work on image …

This project studied the effect of varying the training size for different classification
techniques. The learning curves were then regressed using four common equations. In the …