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Data Interpretation, Statistical

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Computer-Aided Diagnosis System for Alzheimer's Disease Using Different Discrete Transform Techniques.

American journal of Alzheimer's disease and other dementias
The different discrete transform techniques such as discrete cosine transform (DCT), discrete sine transform (DST), discrete wavelet transform (DWT), and mel-scale frequency cepstral coefficients (MFCCs) are powerful feature extraction techniques. Th...

Classifying Regularized Sensor Covariance Matrices: An Alternative to CSP.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Common spatial patterns (CSP) is a commonly used technique for classifying imagined movement type brain-computer interface (BCI) datasets. It has been very successful with many extensions and improvements on the basic technique. However, a drawback o...

Unified-theory-of-reinforcement neural networks do not simulate the blocking effect.

Behavioural processes
For the last 20 years the unified theory of reinforcement (Donahoe et al., 1993) has been used to develop computer simulations to evaluate its plausibility as an account for behavior. The unified theory of reinforcement states that operant and respon...

CRFs based de-identification of medical records.

Journal of biomedical informatics
De-identification is a shared task of the 2014 i2b2/UTHealth challenge. The purpose of this task is to remove protected health information (PHI) from medical records. In this paper, we propose a novel de-identifier, WI-deId, based on conditional rand...

Rediscovery of Good-Turing estimators via Bayesian nonparametrics.

Biometrics
The problem of estimating discovery probabilities originated in the context of statistical ecology, and in recent years it has become popular due to its frequent appearance in challenging applications arising in genetics, bioinformatics, linguistics,...

Maximum margin semi-supervised learning with irrelevant data.

Neural networks : the official journal of the International Neural Network Society
Semi-supervised learning (SSL) is a typical learning paradigms training a model from both labeled and unlabeled data. The traditional SSL models usually assume unlabeled data are relevant to the labeled data, i.e., following the same distributions of...

A literature-driven method to calculate similarities among diseases.

Computer methods and programs in biomedicine
BACKGROUND: "Our lives are connected by a thousand invisible threads and along these sympathetic fibers, our actions run as causes and return to us as results". It is Herman Melville's famous quote describing connections among human lives. To paraphr...

Near-Bayesian Support Vector Machines for imbalanced data classification with equal or unequal misclassification costs.

Neural networks : the official journal of the International Neural Network Society
Support Vector Machines (SVMs) form a family of popular classifier algorithms originally developed to solve two-class classification problems. However, SVMs are likely to perform poorly in situations with data imbalance between the classes, particula...

Weighting training images by maximizing distribution similarity for supervised segmentation across scanners.

Medical image analysis
Many automatic segmentation methods are based on supervised machine learning. Such methods have proven to perform well, on the condition that they are trained on a sufficiently large manually labeled training set that is representative of the images ...

Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity--A multi-center study.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Resting-state functional magnetic resonance imaging studies examining low frequency fluctuations (0.01-0.08 Hz) have revealed atypical whole brain functional connectivity patterns in adolescents with autism spectrum disorder (ASD), and th...