Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
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May 15, 2017 - MATLAB
Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means
Implementation of multinomial logisitic regression, Weighted Logistic Regression, Bayesian Logistic Regression, Gaussian Generative Classification and Gaussian Naive Bayes Classification from scratch in MATLAB
Classifying breast cancer using knn, svm , naive bayes and decision trees on Matlab
Naive Bayes classifiers work by correlating the use of tokens (typically words, or sometimes other things), with spam and non-spam e-mails and then using Bayes' theorem to calculate a probability that an email is or is not spam
Breast cancer classification and evaluation of classifiers using k-fold cross-validation
First assignment of Machine Learning I, regarding the impelementation of a naive bayes classifier in Matlab
Image processing with Matlab.
Machine Learning by Stanford University Taught by Andrew NG.
Naive Bayes for Spam Filtering!
ML classifier for identifying encrypted streaming video traffic.
Machine Learning algorithms in Matlab
This project involved an interrogation of the SVM and Naive-Bayes algorithms with a marine classification data problem posed by Professor Dr. Amber Stubler. Dr. Stubler’s research is concerned with the marine community ecology of kelp and coral reef ecosystems, honing in specifically on Caribbean coral reef recruitment under different sedimentat…
📊 Final project for MATLAB Programming, Spring 2017, Fudan University
Text Classifier Using with Naive Bayes. Machine Learning
Machine learning script in Matlab to compare Random Forest and Naive Bayes Classifier
Wearable Technologies coursework assignment
Implementation of various machine learning classification techniques: K-means clustering, Naive Bayes with Gaussian distributions etc.
Replicates all results from the paper: Benvenuti et al. 2018. "Scale-Invariant Visual Capabilities Explained by Topographic Representations of Luminance and Texture in Primate V1". Neuron 1–9. doi:10.1016/J.NEURON.2018.10.020
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