Popular GitHub Repositories for Support Vector Machine

M. Beyeler (2017). Machine Learning for OpenCV: Intelligent image processing with Python. Packt Publishing Ltd., ISBN 978-178398028-4.

Code for training and test machine learning classifiers on MIT-BIH Arrhyhtmia database

An Architecture Combining Convolutional Neural Network (CNN) and Linear Support Vector Machine (SVM) for Image Classification

目标是提供一个完整的Java机器学习(Machine Learning/ML)框架,作为人工智能在学术界与工业界的桥梁. 让相关领域的研发人员能够在各种软硬件环境/数据结构/算法/模型之间无缝切换. 涵盖了从数据处理到模型的训练与评估各个环节,支持硬件加速和并行计算,是最快最全的Java机器学习库.

Machine learning semantic segmentation - Random Forest, SVM, GBC

Apply modern, deep learning techniques for anomaly detection to identify network intrusions.

R package to tune parameters for machine learning(Support Vector Machine, Random Forest, and Xgboost), using bayesian optimization with gaussian process

Forecasting weather Using Multinomial Logistic Regression, Decision Tree, Naïve Bayes Multinomial, and Support Vector Machine

An interactive approach to understanding Machine Learning using scikit-learn

ML model for stock trend prediction using Python

I have had trouble in the past finding a place where I could learn about statistical learning algorithms, resources as to how to learn them and the code associated with it. This is my attempt at remedying that issue.

Forecasting for Istanbul Stock Exchange with ANFIS and SVM based on Genetic and Ant Colony Algorithm for Parameter Optimization

This is the code repository for my book Python Machine Learning By Example Second Edition (published by Packt, March 2019)

Thanks to digitization, we often have access to large databases, consisting of various fields of information, ranging from numbers to texts and even boolean values. Such databases lend themselves especially well to machine learning, classification and big data analysis tasks. We are able to train classifiers, using already existing data and use them for predicting the values of a certain field, given that we have information regarding the other fields. Most specifically, in this study, we look at the Electronic Health Records (EHRs) that are compiled by hospitals. These EHRs are convenient means of accessing data of individual patients, but there processing as a whole still remains a task. However, EHRs that are composed of coherent, well-tabulated structures lend themselves quite well to the application to machine language, via the usage

Predict which cell is cancerous with 96% accuracy using SVM machine learning algorithm.

Source Code for 'Adversarial Label Flips Attack on Support Vector Machines'

MetroPT-3 Anomaly Detection using Machine Learning and Deep Learning

Perform face recognition in video stream for registered members

Weather Prediction Model to forecast the temperature trends for Seattle,WA using a real time data set from: https://www.ncdc.noaa.gov/cdo-web/

Solutions to Coursera Machine Learning course programming assignments