GitHub

tiskw/random-fourier-features

RFF can be applicable to many other machine learning algorithms than the above. The author will provide implementations of the other algorithms soon. This module supports training/inference on GPU.
Nature

Enhancing anomaly detection in IoT-driven factories using Logistic Boosting, Random Forest, and SVM: A comparative machine learning approach

Three machine learning algorithms—Logistic Boosting, Random Forest, and Support Vector Machines (SVM)—were evaluated for anomaly detection in IoT-driven industrial environments. A real-world dataset ...
IEEE

Buried Magnetic Material Detection System: An SVM Algorithm Application

Abstract: In recent times, studies about remote-sensing methods have focused on improving variables like sensing distance, sensitivity, and power consumption of available remote-sensing methods. The ...
LinkedIn

Hyperparameter Tuning with GridSearchCV: A Hands-On Example with SVM and the Iris Dataset

Hyperparameter tuning is a critical step in optimizing machine learning models for optimal performance. It involves selecting the best combination of hyperparameters, such as regularization strength, ...
IEEE

Naive Bayes Classification Model for the Student Performance Prediction

Abstract: The prediction analysis is the approach which predicts future possibilities from the previous data. The student performance prediction technique has the three phases which are pre-processing ...
Frontiers

Supervised machine learning models for depression sentiment analysis

Globally, the prevalence of mental health problems, especially depression, is at an all-time high. The objective of this study is to utilize machine learning models and sentiment analysis techniques ...
LinkedIn

SVM Parameter Optimization with Python: A Step-by-Step Guide

Support Vector Machines (SVM) are widely used in machine learning for classification and regression tasks. However, the performance of an SVM model depends heavily on its parameter settings, such as ...
C&EN

Study and Classification of Porosity Stress Sensitivity in Shale Gas Reservoirs Based on Experiments and Optimized Support Vector Machine Algorithm for the Silurian Longmaxi ...

TOC can not only generate gas but also provide the main space for gas storage. The structure of the organic matters within the connected and isolated pore network is essential for gas storage capacity ...
Frontiers

Multivariate Pattern Analysis of Lifelong Premature Ejaculation Based on Multiple Kernel Support Vector Machine

A total of fifty-two patients with lifelong PE and 36 matched healthy controls were enrolled in this study. Structural MRI data, functional MRI data, and diffusion tensor imaging (DTI) data were used ...
Scientific Research Publishing

Credit Card Fraud Detection Using Weighted Support Vector Machine ()

Credit card fraudulent data is highly imbalanced, and it has presented an overwhelmingly large portion of nonfraudulent transactions and a small portion of fraudulent transactions. The measures used ...
Analytics India Magazine

A Hands-on Guide To Hybrid Ensemble Learning Models, With Python Code

Ensemble learning combines the strengths of multiple models to enhance performance in classification and regression tasks. Hybrid ensemble models utilise a diverse range of weak learners, differing ...