Clustering and Unsupervised Classification

Richards, John A.

doi:10.1007/978-3-642-88087-2_9

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…The tool has been successfully created, which is useful to classify patients with any type of disease, in this case, dyslexia. In addition, there is a wide range of classifiers since there is at least one of each type: supervised, unsupervised and a combination of both [3][4][5]. At present, this application is an early version, so it is very similar to the basic version, and it can be used as a guide to prevent users from having problems when using this tool.…”

Section: Discussionmentioning

confidence: 99%

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Automatic classification of dyslexic children by applying machine learning to fMRI images

Chimeno

Garcia-Zapirain

Prieto

et al. 2014

Bio-Medical Materials and Engineering

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Functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI) are a source of information to study different pathologies. This tool allows to classify subjects under study, analysing in this case, the functions related to language in young patients with dyslexia. Images are obtained using a scanner and different tests are performed on subjects. After processing the images, the areas that are activated by patients when performing the paradigms or anatomy of the tracts were obtained. The main objective is to ultimately introduce a group of monocular vision subjects, whose brain activation model is unknown. This classification helps to assess whether these subjects are more akin to dyslexic or control subjects. Machine learning techniques study systems that learn how to perform non-linear classifications through supervised or unsupervised training, or a combination of both. Once the machine has been set up, it is validated with the subjects who have not been entered in the training stage. The results are obtained using a user-friendly chart. Finally, a new tool for the classification of subjects with dyslexia and monocular vision was obtained (achieving a success rate of 94.8718% on the Neuronal Network classifier), which can be extended to other further classifications.

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Section: Discussionmentioning

confidence: 99%

“…The learning phase includes supervised [3], unsupervised [4] and semi-supervised [5] learning. Supervised learning develops a mathematical function that, based on the pre-labelled training samples, deduces which category or groups the set of input samples belong to.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification of dyslexic children by applying machine learning to fMRI images

Chimeno

Garcia-Zapirain

Prieto

et al. 2014

Bio-Medical Materials and Engineering

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“…To further clarify the visual and quantitative results derived from the steps given in 2.6.1 further image processing was used. Experimentation was undertaken with unsupervised clustering: k-means, fuzzy C-means, watershed, and ELKI (Environment for Loping KDD-applications supported by Index Structures) [77][78][79][80][81]. Machine learning techniques, Random Forest (RF) [82][83][84][85] and Naïve Bayes were further utilized to classify and provide greater clarity to the feature distribution.…”

Section: Advanced Processingmentioning

confidence: 99%

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

Brooke

Clutterbuck

2019

Remote Sensing

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There is a long history of the use of aerial imagery for archaeological research, but the application of multisensor image data has only recently been facilitated by the development of unmanned aerial vehicles (UAVs). Two archaeological sites in the East Midlands U.K. that differ in age and topography were selected for survey using multisensor imaging from a fixed-wing UAV. The aim of this study was to determine optimum methodology for the use of UAVs in examining archaeological sites that have no obvious surface features and examine issues of ground control target design, thermal effects, image processing and advanced filtration. The information derived from the range of sensors used in this study enabled interpretation of buried archaeology at both sites. For any archaeological survey using UAVs, the acquisition of visible colour (RGB), multispectral, and thermal imagery as a minimum are advised, as no single technique is sufficient to attempt to reveal the maximum amount of potential information.

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A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance

Jeon

Choi

Kim

2004

Lecture Notes in Computer Science

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Clustering and Unsupervised Classification

Cited by 4 publications

References 1 publication

Automatic classification of dyslexic children by applying machine learning to fMRI images

Automatic classification of dyslexic children by applying machine learning to fMRI images

Mapping Heterogeneous Buried Archaeological Features Using Multisensor Data from Unmanned Aerial Vehicles

A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance

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