polynomial-kernel

DanShai/kernalized-tsne

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

ahcen23/x-DTT

Easy to use x-DTT MATLAB package for DTT and Integer DTT transform kernel generation

saipavani68/SVM

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.

SaumyaBhandari/Support_Vector_Machine

A SVM classifier created to classify data on the IRIS dataset. A linear SVM as well as a Radial Basis SVM was created and also a polynomial kernel was created which classifies the data correctly to their original class.

Gajju4/Heart-Disease-Prediction-using-Support-Vector-Machine.

Created a model from scratch (without using any libraries) to predict whether a person have a heart diseases using support vector machine. and then compare the model's accuracy with model created using Sklearn library.

lurenss/Spam-filter

Second assignment of Artificial Intelligence course held by Professor Andrea Torsello of Ca' Foscari University of Venice, spam detectors with SVM classification using linear, polynomial of degree 2, RBF kernels and Naive Bayes and k-NN

Parashar7/Support_Vector_Machine

SVMs are used for Classification as well as Regression problems. However, it is primarily used for Classification problems. #%% md # The Technique (Support Vector Machine) Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane.

SaraLittleSquirrel/Obesity-estimator

Project for Machine Learning Data Mining course

Sushama-Rangarajan/SupportVectorMachines

Hyperparameter tuning using Support Vector Machine kernels

ZahirAhmadChaudhry/Pulsar_dataset_Classification_using_SVM

This project focuses on classifying pulsar stars using the Support Vector Machine (SVM) algorithm, a powerful method in the realm of supervised learning. The goal is to automate the identification process of pulsar stars from candidates collected during surveys, based on predictive modeling.

AyanPahari/Soft-Margin-SVM

We will apply soft-margin SVM to handwritten digits from the processed US Postal Service Zip Code data set.

being-aerys/Machine_Learning_CS534_Fall-2018

Linear Regression with L2 Regularization, Online, Average, and Polynomial Kernel Perceptron for Optical Character Recognition, Decision Tree Ensemble, Random Forest, AdaBoost

esra-polat/learning-from-data-homeworks

Gulshank0719/Support-vector-machine

Building a smodel using SVC

Gulshank0719/Support-Vector-Machines-Classifier

Support Vector Machines (SVMs in short) are supervised machine learning algorithms that are used for classification and regression purposes. In this kernel, I have build a Support Vector Machines classifier to classify a Pulsar star. I have used the Predicting a Pulsar Star dataset for this project.

opeajibuwa/Sentiment-Analysis-with-Support-Vector-Machines

Classification of tweets as positive or negative sentiments using different SVM kernels

SCUS3/Wage-Regression-Analysis

This project provides a comprehensive guide to implementing PCA from scratch and validating it using scikit-learn's implementation. The visualizations help in understanding the data's variance and the effectiveness of dimensionality reduction.

znreza/supervised_classification

This repository contains codes for running naive bayes and k-NN classification algorithms on large dataset in python