AIMC Topic: Unsupervised Machine Learning

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Unsupervised learning of perceptual feature combinations.

PLoS computational biology
In many situations it is behaviorally relevant for an animal to respond to co-occurrences of perceptual, possibly polymodal features, while these features alone may have no importance. Thus, it is crucial for animals to learn such feature combination...

Clustering honey samples with unsupervised machine learning methods using FTIR data.

Anais da Academia Brasileira de Ciencias
This study utilizes Fourier transform infrared (FTIR) data from honey samples to cluster and categorize them based on their spectral characteristics. The aim is to group similar samples together, revealing patterns and aiding in classification. The p...

Multi-armed bandits, Thomson sampling and unsupervised machine learning in phylogenetic graph search.

Cladistics : the international journal of the Willi Hennig Society
A phylogenetic graph search relies on a large number of highly parameterized search procedures (e.g. branch-swapping, perturbation, simulated annealing, genetic algorithm). These procedures vary in effectiveness over datasets and at alternative point...

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Pediatric research
BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hind...

Exploring subtypes of multiple sclerosis through unsupervised machine learning of automated fiber quantification.

Japanese journal of radiology
PURPOSE: This study aimed to subtype multiple sclerosis (MS) patients using unsupervised machine learning on white matter (WM) fiber tracts and investigate the implications for cognitive function and disability outcomes.

Identifying definite patterns of unmet needs in patients with multiple sclerosis using unsupervised machine learning.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
INTRODUCTION: People with multiple sclerosis (PwMS) exhibit a spectrum of needs that extend beyond solely disease-related determinants. Investigating unmet needs from the patient perspective may address daily difficulties and optimize care. Our aim w...

UDRSNet: An unsupervised deformable registration module based on image structure similarity.

Medical physics
BACKGROUND: Image registration is a challenging problem in many clinical tasks, but deep learning has made significant progress in this area over the past few years. Real-time and robust registration has been made possible by supervised transformatio...

How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Journal of epidemiology and global health
Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple ...

[Feeling analysis on allergen immunotherapy on using an unsupervised machine learning model].

Revista alergia Mexico (Tecamachalco, Puebla, Mexico : 1993)
OBJECTIVE: Analyze feelings about allergen-specific immunotherapy on using the VADER model VADER () model.

A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer.

Computers in biology and medicine
The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm over...