AIMC Topic: Bayes Theorem

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Active Learning of Bayesian Linear Models with High-Dimensional Binary Features by Parameter Confidence-Region Estimation.

Neural computation Aug 14, 2020
In this letter, we study an active learning problem for maximizing an unknown linear function with high-dimensional binary features. This problem is notoriously complex but arises in many important contexts. When the sampling budget, that is, the num...
Confidence Intervals Linear Models Bayes Theorem Supervised Machine Learning Humans
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Synthesis of recurrent neural dynamics for monotone inclusion with application to Bayesian inference.

Neural networks : the official journal of the International Neural Network Society Aug 12, 2020
We propose a top-down approach to construct recurrent neural circuit dynamics for the mathematical problem of monotone inclusion (MoI). MoI in a general optimization framework that encompasses a wide range of contemporary problems, including Bayesian...
Neural Networks, Computer Bayes Theorem Markov Chains
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Improving the accuracy of medical diagnosis with causal machine learning.

Nature communications Aug 11, 2020
Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis...
Bayes Theorem Data Collection Decision Making Models, Statistical Diagnosis Data Accuracy Disease Humans Machine Learning Algorithms Diagnosis, Computer-Assisted
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Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

The Journal of thoracic and cardiovascular surgery Aug 10, 2020
BACKGROUND: Interest in the usefulness of machine learning (ML) methods for outcomes prediction has continued to increase in recent years. However, the advantage of advanced ML model over traditional logistic regression (LR) remains controversial. We...
Humans Machine Learning Logistic Models Bayes Theorem Cardiac Surgical Procedures Prognosis
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Long-distance disorder-disorder relation extraction with bootstrapped noisy data.

Journal of biomedical informatics Aug 7, 2020
OBJECTIVE: Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-dista...
Data Mining Artificial Intelligence Algorithms Databases, Factual Bayes Theorem
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Predicting Absenteeism and Temporary Disability Using Machine Learning: a Systematic Review and Analysis.

Journal of medical systems Aug 7, 2020
The main objective of this paper is to present a systematic analysis and review of the state of the art regarding the prediction of absenteeism and temporary incapacity using machine learning techniques. Moreover, the main contribution of this resear...
Machine Learning Brazil Humans Australia Bayes Theorem Absenteeism Saudi Arabia India
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Design of Festival Sentiment Classifier Based on Social Network.

Computational intelligence and neuroscience Aug 7, 2020
With the development of society, more and more attention has been paid to cultural festivals. In addition to the government's emphasis, the increasing consumption in festivals also proves that cultural festivals are playing increasingly important rol...
China Public Opinion Holidays Emotions Machine Learning Social Networking Humans Language Bayes Theorem Neural Networks, Computer Culture
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Twin minimax probability machine for pattern classification.

Neural networks : the official journal of the International Neural Network Society Aug 1, 2020
We propose a new distribution-free Bayes optimal classifier, called the twin minimax probability machine (TWMPM), which combines the benefits of both minimax probability machine(MPM) and twin support vector machine (TWSVM). TWMPM tries to construct t...
Databases, Factual Support Vector Machine Pattern Recognition, Automated Bayes Theorem
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Survivability modelling using Bayesian network for patients with first and secondary primary cancers.

Computer methods and programs in biomedicine Aug 1, 2020
BACKGROUND AND OBJECTIVE: Multiple primary cancers significantly threat patient survivability. Predicting the survivability of patients with two cancers is challenging because its stochastic pattern relates with numerous variables.
Humans Neural Networks, Computer Bayes Theorem Prognosis Neoplasms Logistic Models Taiwan
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Deep learning using preoperative magnetic resonance imaging information to predict early recovery of urinary continence after robot-assisted radical prostatectomy.

International journal of urology : official journal of the Japanese Urological Association Jul 30, 2020
OBJECTIVES: To investigate whether a deep learning model from magnetic resonance imaging information is an accurate method to predict the risk of urinary incontinence after robot-assisted radical prostatectomy.
Humans Robotic Surgical Procedures Prostatectomy Magnetic Resonance Imaging Bayes Theorem Prostatic Neoplasms Recovery of Function Male Robotics Deep Learning
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