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Probability

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Valid Probabilistic Predictions for Ginseng with Venn Machines Using Electronic Nose.

Sensors (Basel, Switzerland)
In the application of electronic noses (E-noses), probabilistic prediction is a good way to estimate how confident we are about our prediction. In this work, a homemade E-nose system embedded with 16 metal-oxide semi-conductive gas sensors was used t...

Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
BACKGROUND AND PURPOSE: Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatia...

Predicting the Direction of Stock Market Index Movement Using an Optimized Artificial Neural Network Model.

PloS one
In the business sector, it has always been a difficult task to predict the exact daily price of the stock market index; hence, there is a great deal of research being conducted regarding the prediction of the direction of stock price index movement. ...

A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.

Computational intelligence and neuroscience
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each me...

Binary classification SVM-based algorithms with interval-valued training data using triangular and Epanechnikov kernels.

Neural networks : the official journal of the International Neural Network Society
Classification algorithms based on different forms of support vector machines (SVMs) for dealing with interval-valued training data are proposed in the paper. L2-norm and L∞-norm SVMs are used for constructing the algorithms. The main idea allowing u...

A novel method to use fuzzy soft sets in decision making based on ambiguity measure and Dempster-Shafer theory of evidence: An application in medical diagnosis.

Artificial intelligence in medicine
OBJECTIVE: Recently, fuzzy soft sets-based decision making has attracted more and more interest. Although plenty of works have been done, they cannot provide the uncertainty or certainty of their results. To manage uncertainty is one of the most impo...

Learning With Jensen-Tsallis Kernels.

IEEE transactions on neural networks and learning systems
Jensen-type [Jensen-Shannon (JS) and Jensen-Tsallis] kernels were first proposed by Martins et al. (2009). These kernels are based on JS divergences that originated in the information theory. In this paper, we extend the Jensen-type kernels on probab...

Calibrating random forests for probability estimation.

Statistics in medicine
Probabilities can be consistently estimated using random forests. It is, however, unclear how random forests should be updated to make predictions for other centers or at different time points. In this work, we present two approaches for updating ran...

An EEG-Based Fuzzy Probability Model for Early Diagnosis of Alzheimer's Disease.

Journal of medical systems
Alzheimer's disease is a degenerative brain disease that results in cardinal memory deterioration and significant cognitive impairments. The early treatment of Alzheimer's disease can significantly reduce deterioration. Early diagnosis is difficult, ...

ProbOnto: ontology and knowledge base of probability distributions.

Bioinformatics (Oxford, England)
MOTIVATION: Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of pro...