Abstract: An automated, robust, noncontact sleep posture recognition technique is proposed in this letter, which uses optimizable (Bayesian hyperparameter tuning) machine learning (ML) classifiers ...
In this blog post, I am going to teach you how to train a Bayesian deep learning classifier using Keras and tensorflow. Before diving into the specific training example, I will cover a few important ...
Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...
ABSTRACT: Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy ...
Abstract: K-dependence Bayesian network classifier(KDB) has been widely used in data mining and machine learning. To enhance the expression ability and classification performance of KDB, the present ...
Machine intelligence (MI), including machine learning and deep learning, have been regarded as promising methods to reduce the prohibitively high cost of drug development. However, a dilemma within MI ...
External Validation of the Bone Metastases Ensemble Trees for Survival (BMETS) Machine Learning Model to Predict Survival in Patients With Symptomatic Bone Metastases Patient-level data from the ...
Bayesian Networks are graphical models useful for various applications, including time series prediction and anomaly detection. Bayesian inference offers a robust set of tools for modelling ...
Explaining the output of a complex machine learning (ML) model often requires approximation using a simpler model. To construct interpretable explanations that are also consistent with the original ML ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results