AIMC Topic: Normal Distribution

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Analysis of short-term heart rate and diastolic period variability using a refined fuzzy entropy method.

Biomedical engineering online
BACKGROUND: Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term vari...

A new robust model of one-class classification by interval-valued training data using the triangular kernel.

Neural networks : the official journal of the International Neural Network Society
A robust one-class classification model as an extension of Campbell and Bennett's (C-B) novelty detection model on the case of interval-valued training data is proposed in the paper. It is shown that the dual optimization problem to a linear program ...

Many regression algorithms, one unified model: A review.

Neural networks : the official journal of the International Neural Network Society
Regression is the process of learning relationships between inputs and continuous outputs from example data, which enables predictions for novel inputs. The history of regression is closely related to the history of artificial neural networks since t...

Image Quality Assessment Using Human Visual DOG Model Fused With Random Forest.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Objective image quality assessment (IQA) plays an important role in the development of multimedia applications. Prediction of IQA metric should be consistent with human perception. The release of the newest IQA database (TID2013) challenges most of t...

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

Neural networks : the official journal of the International Neural Network Society
Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation proce...

Incremental learning for ν-Support Vector Regression.

Neural networks : the official journal of the International Neural Network Society
The ν-Support Vector Regression (ν-SVR) is an effective regression learning algorithm, which has the advantage of using a parameter ν on controlling the number of support vectors and adjusting the width of the tube automatically. However, compared to...

A biological mechanism for Bayesian feature selection: Weight decay and raising the LASSO.

Neural networks : the official journal of the International Neural Network Society
Biological systems are capable of learning that certain stimuli are valuable while ignoring the many that are not, and thus perform feature selection. In machine learning, one effective feature selection approach is the least absolute shrinkage and s...

A rational model of function learning.

Psychonomic bulletin & review
Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We pr...

A Comparison of SVM and GMM-Based Classifier Configurations for Diagnostic Classification of Pulmonary Sounds.

IEEE transactions on bio-medical engineering
GOAL: The aim of this study is to find a useful methodology to classify multiple distinct pulmonary conditions including the healthy condition and various pathological types, using pulmonary sounds data.

Bayes optimal template matching for spike sorting - combining fisher discriminant analysis with optimal filtering.

Journal of computational neuroscience
Spike sorting, i.e., the separation of the firing activity of different neurons from extracellular measurements, is a crucial but often error-prone step in the analysis of neuronal responses. Usually, three different problems have to be solved: the d...