cat🐈: the repo for the paper "Embarrassingly Simple Unsupervised Aspect extraction"

C++ Implementation of the RBF (Radial Basis Function) Network and choosing centroids using K-Means++

ProtVec can be used in protein interaction predictions, structure prediction, and protein data visualization.

Predicting Loan State with SVM method, preprocessing and feature selction

I apply machine learning (ML) techniques to Snowplow web event data to understand how variation in marketing site experiences might correlate to customer conversion.

Empowering Scientific Research with AI Assistance! Open Source Code for Data-Driven Dimensional Analysis.

Training-free Neural Architecture Search (NAS) Using Radial Basis Function (RBF) Kernel

Image Processing and classification using Machine Learning : Image Classification using Open CV and SVM machine learning model

To deal with non-linearly separable we use SVM's Kernel Trick which maps data to higher dimension!

Machine Learning Code Implementations in Python

SPPU - BE ENTC (2015 Pattern) - Elective III

Project ini dibuat untuk memenuhi syarat meraih gelar Sarjana Komputer, Dengan melakukan Klasifikasi Ekspresi Wajah Manusia menggunakan algoritme Local Binary Pattern (LBP) untuk ekstraksi fitur dan Support Vector Machine untuk klasifikasi.

Numpy based implementation of kernel based SVM

Access the Linear or RBF kernel SVM from OCaml using the R e1071 or svmpath packages

MATLAB implementations of different learning methods for Radial Basis Functions (RBF)

Generalized Improved Second Order RBF Neural Network with Center Selection using OLS

Application shows advantage of Classical MRAC using RBFs over PD control when unmodeled dynamics are present in the system (wing rock model).

💚 A heart disease classifier using 4 SVM kernels and decision trees, with PCA, ROC, pruning, grid search cv, confusion matrix, and more

kernalized t-Distributed Stochastic Neighbor Embedding (t-SNE)

Mobile Price Range Prediction: Use sales data to build a classification model for mobile phone price ranges. Features include battery power, camera, memory, and connectivity. Split data, apply logistic regression, KNN, SVM (linear and rbf), and evaluate using confusion matrices. Select the most accurate model.