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

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

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

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

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

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

PCA applied on images and Naive Bayes Classifier to classify them. Validation, cross validation and grid search with multi class SVM

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.

In This Notebook I've build a Machine-Learning model that normalize region names in Damascus city, then I use it in Locator class.

In this project, I predict whether a patient has a low or high chance of having a heart attack using classification

GISETTE is a handwritten digit recognition problem. The problem is to separate the highly confusible digits ‘4’ and ‘9’. This dataset is one of five datasets of the NIPS 2003 feature selection challenge.

Prediction of diabetes health indicators for machine learning class final project

This project is to build a model that predicts the human activities such as Walking, Walking Upstairs, Walking Downstairs, Sitting, Standing or Laying using readings from the sensors on a smartphone carried by the user.

This code reads a dataset i.e, "Heart.csv". Preprocessing of dataset is done and we divide the dataset into training and testing datasets. Linear, rbf and Polynomial kernel SVC are applied and accuracy scores are calculated on the test data. Also, a graph is plotted to show change of accuracy with change in "C" value.

R scripts for RF and RBF-SVM for Acute Myeloid Leukemia subtypes multiclass classification using gene expression profiles. LASSO feature selection, SMOTE sampling, 10-fold cross-validation, variable importance plot, PCA plot, normalized Confusion Matrix, GSE13159.

Solving the Character recognition problem as an SVM optimization problem using CVXOPT

This aims to perform sentiment analysis on COVID-19 tweets using various classification models. We preprocess the data, convert words to vectors, and train models such as Naïve Bayes, SVM, and KNN. Finally, we compare their performance to determine the most accurate model for predicting sentiment in COVID-19 tweets.