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We compare modern machine learning (MML) techniques to ordinary least squares (OLS) regression on out‐of‐sample (OOS) operational validity, adverse impact, and dropped predictor counts within a common selection scenario: the prediction of job performance from a battery of diverse psychometrically‐validated tests. In total, scores from 1.2 billion validation study participants were simulated to describe...
The purpose of this pilot study is to determine whether a deep convolutional neural network can be trained with limited image data to detect high-grade small bowel obstruction patterns on supine abdominal radiographs. Grayscale images from 3663 clinical supine abdominal radiographs were categorized into obstructive and non-obstructive categories independently by three abdominal radiologists, and the...
Nonnegative Matrix Factorization (NMF), defined as factorizing a nonnegative matrix into two nonnegative factor matrices, is a particularly important problem in machine learning. Unfortunately, it is also ill-posed and NP-hard. We propose a fast, robust, and provably correct algorithm, namely Gradient Vertex Pursuit (GVP), for solving a well-defined instance of the problem which results in a unique...
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