AIMC Topic: Probability

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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...

Effect of network architecture on burst and spike synchronization in a scale-free network of bursting neurons.

Neural networks : the official journal of the International Neural Network Society
We investigate the effect of network architecture on burst and spike synchronization in a directed scale-free network (SFN) of bursting neurons, evolved via two independent α- and β-processes. The α-process corresponds to a directed version of the Ba...

Regular expressions for decoding of neural network outputs.

Neural networks : the official journal of the International Neural Network Society
This article proposes a convenient tool for decoding the output of neural networks trained by Connectionist Temporal Classification (CTC) for handwritten text recognition. We use regular expressions to describe the complex structures expected in the ...

Adapting machine learning techniques to censored time-to-event health record data: A general-purpose approach using inverse probability of censoring weighting.

Journal of biomedical informatics
Models for predicting the probability of experiencing various health outcomes or adverse events over a certain time frame (e.g., having a heart attack in the next 5years) based on individual patient characteristics are important tools for managing pa...

Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.

Applied clinical informatics
OBJECTIVES: Discharging patients from the Neonatal Intensive Care Unit (NICU) can be delayed for non-medical reasons including the procurement of home medical equipment, parental education, and the need for children's services. We previously created ...