Search Results for "pso-svm"
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Showing 74 open source projects for "pso-svm"
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Yeastar: Business Phone System and Unified CommunicationsUser-friendly, optimized, and scalable, the Yeastar P-Series Phone System redefines business connectivity by bringing together calling, meetings, omnichannel messaging, and integrations in one simple platform—removing the limitations of distance, platforms, and systems.
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LIBSVM.jl
LIBSVM bindings for Julia
LIBSVM bindings for Julia. This is a Julia interface for LIBSVM and for the linear SVM model provided by LIBLINEAR. Supports all LIBSVM models: classification C-SVC, nu-SVC, regression: epsilon-SVR, nu-SVR and distribution estimation: one-class SVM. Model objects are represented by Julia-type SVM which gives you easy access to model features and can be saved e.g. as JLD file. -
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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Evolutionary Computation Framework
C++ framework for application of any type of evolutionary computation.
ECF is a framework intended for application of any type of evolutionary computation (GA/GP, DE, Clonalg, ES, PSO, ABC, GAn, local search...). It offers simplicity for the end-user (parameterless usage, tutorial) and customization for experienced EC practicioners. -
EHS Software and Management SystemERA Environmental Software Solutions develops web-based EHS management software for small, medium, and large manufacturers needing to comply with federal, provincial, and state regulations, monitor their air, water, and waste emissions and other environmental outputs, author and manage Safety Data Sheets (SDS) in more than 40 languages, or standardize their Health and Safety procedures for incident and inspection tracking, training delivery, and audit management. The platform also supports comprehensive reporting for programs like TRI, Tier II, Title V, NEI, and NPRI. Companies across the automotive, aerospace, general manufacturing, and paints and coatings industries, to name a few, rely on ERA’s all-in-one, SOC 2 Type II certified SaaS for complete coverage of their EHS needs.
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m2cgen
Transform ML models into a native code
m2cgen (Model 2 Code Generator) - is a lightweight library that provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#, Rust, Elixir). Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies. Some models force input data to be particular type during prediction phase... -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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Smart Algorithm
Repository implementing a variety of intelligent algorithms
...The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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SwaNN
PSO for neural networks
SwaNN is a basic framework for neural networks based on particle swarm optimization (using the Python package PySwarms (https://pyswarms.readthedocs.io/en/latest/). The zip file contains the main programs in SwaNN.py and around 30 examples : - classification - regression - time series forecasting I need some help for class building (I am not an expert in Python nor in OOP), if somebody is interested in it... In Google Colab :... -
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Transform months of data modeling and coding into days.Efficiently model your business and data models, and generate code for your data pipelines, data lakehouse, and analytical applications
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FCML
A machine code manipulation library for Intel 64 and IA-32.
...- Support for the Intel and AT&T syntax - An instruction renderer - An instruction parser - Instructions represented as generic models - UNIX/GNU/Linux and Windows support - Portable - written entirely in C (no external dependencies) - C++ wrapper - Supported instruction sets: MMX, 3D-Now!, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, SSE4A, AVX, AVX2, AES, TBM, BMI1, BMI2, HLE, ADX, CLMUL, RDRAND, RDSEED, FMA, FMA4, LWP, SVM, XOP, VMX, SMX, AVX-512 Source code moved to: https://github.com/swojtasiak/fcml-lib -
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Active Learning
Framework and examples for active learning with machine learning model
...It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
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PSOVina 2.0
The Hybrid Particle Swarm Optimization for Protein-Ligand Docking
A fast docking tool based on the efficient optimization algorithm of Particle Swarm Intelligence and the framework of AutoDock Vina. Based on the initial PSO implementation, our PSOVina method has undergone several important improvements to enhance the docking accuary and achieve remarkable efficiency as compared to the original AutoDock Vina. https://cbbio.online/software/psovina/index.html -
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JSiteDescriptor
Binding site descriptor generation for SVM based classification.
A set of java programs that extract coordinate and chemical information from PDB files. The binding site regions are extracted using grid based scheme. For binding site, spatio-chemical descriptor is generated based on PocketMatch algorithm of Dr. Kalidas (author of this project too). -
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GPU Machine Learning Library. This library aims to provide machine learning researchers and practitioners with a high performance library by taking advantage of the GPU enormous computational power. The library is developed in C++ and CUDA.
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EEG Seizure Prediction
Seizure prediction from EEG data using machine learning
The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy. -
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PrAS
predict protein amidation sites
This predictor is developed to predict amidation sites based on support vector machine (SVM) classifier. It is supplied in source code form along with the required data files and run under the linux. The input is a protein sequence file (fasta format) by Tong Wang and Wei Zheng (tongwang.scu@gmail.com and jlspzw139@sina.com) Notice:You should download all zip file in this project! -
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KA-predictor
lysine acetylation site prediction
This predictor is developed to predict species-specific lysine acetylation sites based on support vector machine (SVM) classifier. It is supplied in source code form along with th e required data files and run under the linux. The input is a protein sequence file (fasta format). -
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Excel VBA Particle Swarm Optimization
A small tool for optimization of complex problems.
This is a Particle Swarm Optimization tool written in VBA for Excel. It is easy to modify in order to enable the users to code their own objective function, constraints and way to handle constraints. Published under licence CC-BY-SA 4.0 -
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UBotSim
Robot Simulator for UBot Self-reconfigurable Robot
A simulation and evolution platform for UBot self-reconfigurable robot. More functions will come as soon as possible. Used libraries: MYGui3.2.0; OGRE1.8.0; Physx3.2; MTparser; GAlib;and other EAs including PSO, GEP etc. Precompiled SDK download site：http://dl.vmall.com/c07d135mza Operation System: Windows (Win 7 is prefered). Delveloping tool (IDE):Visual Studio 2010. -
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classify-20-NG-with-4-ML-Algo
Problem involves classifying 20000 messages into different 20 classes
...Out of all the methods, SVM using the Libsvm [1] produced the most accurate and optimized result for its classification accuracy for the 20 classes. All the algorithm implementation was written Matlab. Download the code and Report here. -
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PSO database
It displays various data from the game Phantasy Star Online
This program displays information from various files from Phantasy Star Online such as itempmt.prs itemRT.afs and itemPT.afs in a cohesive way -
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lncRScan-SVM
A package for lncRNA prediction
The package is used to classify protein coding and long non-coding RNA (lncRNA) transcripts using support vector machine (SVM). -
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SVMBenchmark
CUDA SVM training benchmark
This application can train SVM using LibSVM and several CUDA implementations. Supported input file formats are LibSVM text file and Bottou's LaSVM binary file. Wanted implementation can be chosen using command line parameter. Training, input data loading and output data saving times are measured and reported. Output model is saved in LibSVM text format.