Description: A basic PCA classifier is provided here for a two class classification problem.
An example is given, with some multimodal MRI scans from Multiple Sclerosis patients, in which the brain lesions of two patients are annotated and in the third are detected by the PCA model.
- [scm-jmlr] - on SCM's a review article, SCM than SVM
- [AI_prog] - This is a very simple genetic algorithm
- [Prediction_RBF] - matlab prepared chaotic time series base
- [classification] - The package to achieve a number of commo
- [som] - MRI Brain Tumour Classification- SOM ( S
- [pca_knn] - The method of feature extraction using p
- [PCA_SVM] - This method uses the classical PCA on th
File list (Check if you may need any files):
PCA_classifier\license.txt
..............\PCA_classifier_version1b\apply_model.m
..............\........................\example_classifier_ms.m
..............\........................\get_feature_vectors.c
..............\........................\get_feature_vectors.m
..............\........................\Literature\MICCAI_MS_Challenge_UTwente_Final.pdf
..............\........................\Literature
..............\........................\TestData\patient3_FLAIR.png
..............\........................\........\patient3_T1.png
..............\........................\........\patient3_T2.png
..............\........................\TestData
..............\........................\.rainingData\patient1_FLAIR.png
..............\........................\............\patient1_lesion.png
..............\........................\............\patient1_T1.png
..............\........................\............\patient1_T2.png
..............\........................\............\patient2_FLAIR.png
..............\........................\............\patient2_lesion.png
..............\........................\............\patient2_T1.png
..............\........................\............\patient2_T2.png
..............\........................\TrainingData
..............\........................\train_model.m
..............\PCA_classifier_version1b
PCA_classifier
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