LACourtenay/Support-Vector-Machine-for-Morphological-Analysis

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Updated over 1 year ago

LACourtenay/Support-Vector-Machine-for-Morphological-Analysis

LACourtenay

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Support Vector Machines (SVM) are powerful Machine Learning algorithms that can provide efficient and high performing classifications of complex data sets. Here lies R-Code that can be used to process morphological data using SVMs that have been able to achieve 100% classification rates in some practical cases.

GraphGMM

An extension to geometric morphometric libraries oriented towards the integration of Graph Theory into the analysis of landmark data. Graph-based Geometric Morphometrics is additionally accompanied by other non-linear dimensionality reduction algorithms.

HyperSkinCare_DL

Code used for the training of Convolutional Neural Support Vector Machines (CNSVM) for the classification of Hyperspectral signatures obtained from Basal Cell Carcinoma Skin Cancer patients

Carnivore_Tooth_Pit_Classification

Source code and Datasets from the article "Developments in Data Science Solutions for Carnivore Tooth Pit Classification"

Jupyter Notebook

HyperSkinCare_Statistics

Code used for the robust statistical analysis of Hyperspectral signatures obtained from Non-Melanoma Skin Cancer patients

Handaxe_Simulation

Code used for the simulation of theoretical handaxes

GMM_Measurement_Accuracy_Tools

Statistical tools and methods for the evaluation of measurement error and accuracy in geometric morphometric analyses

Deep-Neural-Network-for-Cut-Mark-Classification

Neural Networks are highly powerful and versatile algorithms that can be trained to complex and large datasets. Here lies the code and details used to design a Neural Network that differentiates anthropic butchery traces from naturally produced trampling damage on bones

Jupyter Notebook