AIMC Topic:
Supervised Machine Learning

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Distant supervision for neural relation extraction integrated with word attention and property features.

Neural networks : the official journal of the International Neural Network Society Jan 31, 2018
Distant supervision for neural relation extraction is an efficient approach to extracting massive relations with reference to plain texts. However, the existing neural methods fail to capture the critical words in sentence encoding and meanwhile lack...
Semantics Supervised Machine Learning Attention
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Viscosity Prediction in a Physiologically Controlled Ventricular Assist Device.

IEEE transactions on bio-medical engineering Jan 24, 2018
OBJECTIVE: We present a novel machine learning model to accurately predict the blood-analog viscosity during support of a pathological circulation with a rotary ventricular assist device (VAD). The aim is the continuous monitoring of the hematocrit (...
Humans Algorithms Signal Processing, Computer-Assisted Supervised Machine Learning Hematocrit Databases, Factual Hemodynamics Monitoring, Physiologic Models, Cardiovascular Heart-Assist Devices Viscosity
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Semi-Supervised Discriminative Classification Robust to Sample-Outliers and Feature-Noises.

IEEE transactions on pattern analysis and machine intelligence Jan 17, 2018
Discriminative methods commonly produce models with relatively good generalization abilities. However, this advantage is challenged in real-world applications (e.g., medical image analysis problems), in which there often exist outlier data points (sa...
Neuroimaging Neurodegenerative Diseases Supervised Machine Learning Image Interpretation, Computer-Assisted Humans Algorithms Databases, Factual Brain ROC Curve Discriminant Analysis
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Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels.

Computer methods and programs in biomedicine Jan 11, 2018
BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) co...
Diffusion Tensor Imaging Brain Neoplasms Magnetic Resonance Imaging Multimodal Imaging Neoplasm Grading Humans Supervised Machine Learning Datasets as Topic Algorithms
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Supervised Machine Learning for Population Genetics: A New Paradigm.

Trends in genetics : TIG Jan 10, 2018
As population genomic datasets grow in size, researchers are faced with the daunting task of making sense of a flood of information. To keep pace with this explosion of data, computational methodologies for population genetic inference are rapidly be...
Humans Supervised Machine Learning Selection, Genetic Biological Evolution Genetics, Population Data Mining Datasets as Topic Genome, Human
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Neuroanatomical heterogeneity of schizophrenia revealed by semi-supervised machine learning methods.

Schizophrenia research Dec 21, 2017
UNLABELLED: Schizophrenia is associated with heterogeneous clinical symptoms and neuroanatomical alterations. In this work, we aim to disentangle the patterns of neuroanatomical alterations underlying a heterogeneous population of patients using a se...
Pattern Recognition, Automated Magnetic Resonance Imaging Female Male Humans Multivariate Analysis Supervised Machine Learning Young Adult Cohort Studies Brain Schizophrenia Adult Image Processing, Computer-Assisted
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Using diffusion MRI to discriminate areas of cortical grey matter.

NeuroImage Dec 21, 2017
Cortical area parcellation is a challenging problem that is often approached by combining structural imaging (e.g., quantitative T1, diffusion-based connectivity) with functional imaging (e.g., task activations, topological mapping, resting state cor...
Humans Adult Image Processing, Computer-Assisted Supervised Machine Learning Connectome Neuroimaging Diffusion Tensor Imaging Gray Matter Diffusion Magnetic Resonance Imaging Cerebral Cortex
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A functional supervised learning approach to the study of blood pressure data.

Statistics in medicine Dec 20, 2017
In this work, a functional supervised learning scheme is proposed for the classification of subjects into normotensive and hypertensive groups, using solely the 24-hour blood pressure data, relying on the concepts of Fréchet mean and Fréchet variance...
Hypotension Blood Pressure Nonlinear Dynamics Humans Biometry Algorithms Supervised Machine Learning Hypertension Databases, Factual Blood Pressure Determination Models, Statistical
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Supervised learning in spiking neural networks with FORCE training.

Nature communications Dec 20, 2017
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here...
Songbirds Vocalization, Animal Models, Neurological Supervised Machine Learning Nerve Net Learning Animals
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Sparsifying machine learning models identify stable subsets of predictive features for behavioral detection of autism.

Molecular autism Dec 19, 2017
BACKGROUND: Autism spectrum disorder (ASD) diagnosis can be delayed due in part to the time required for administration of standard exams, such as the Autism Diagnostic Observation Schedule (ADOS). Shorter and potentially mobilized approaches would h...
Humans Logistic Models Female Algorithms Supervised Machine Learning Autism Spectrum Disorder Male Models, Psychological ROC Curve
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