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Visar Berisha

Researcher at Arizona State University

Publications -  172
Citations -  2083

Visar Berisha is an academic researcher from Arizona State University. The author has contributed to research in topics: Intelligibility (communication) & Computer science. The author has an hindex of 20, co-authored 148 publications receiving 1454 citations. Previous affiliations of Visar Berisha include University of Arizona & Massachusetts Institute of Technology.

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Proceedings ArticleDOI

Sparse representations for automatic target classification in SAR images

TL;DR: Results show that the performance of the algorithm is superior to using a support vector machines based approach with similar assumptions, and significant complexity reduction is obtained by reducing the dimensions of the data using random projections for only a small loss in performance.
Journal ArticleDOI

Empirically Estimable Classification Bounds Based on a Nonparametric Divergence Measure

TL;DR: It is shown that a nonparametric f-divergence measure can be used to provide improved bounds on the minimum binary classification probability of error for the case when the training and test data are drawn from the same distribution.
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Persistent post-traumatic headache vs. migraine: an MRI study demonstrating differences in brain structure

TL;DR: Differences in regional volumes, cortical thickness, surface area and brain curvature were identified when comparing the group of individuals with persistent post-traumatic headache to the group with migraine, suggesting differences in their underlying pathophysiology.
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Tracking discourse complexity preceding Alzheimer's disease diagnosis: a case study comparing the press conferences of Presidents Ronald Reagan and George Herbert Walker Bush.

TL;DR: A method to extract key features from discourse transcripts from President Ronald Reagan and President George Herbert Walker Bush, who have no known diagnosis of Alzheimer's disease, were described and regression analyses were conducted.
Journal ArticleDOI

Automatic assessment of vowel space area.

TL;DR: An automated algorithm is described using healthy, connected speech rather than single syllables and estimates the entire vowel working space rather than corner vowels, revealing a strong correlation between the traditional VSA and automated estimates.