Search Results for "svm"
Sort By:
Showing 64 open source projects for "svm"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
1
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. -
2
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... -
3
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. -
4
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... -
Photo and Video Editing APIs and SDKsUnlock Picsart's full editing suite by embedding our Editor SDK directly into your platform. Offer your users the power of a full design suite without leaving your site.
-
5
-
6
FCML
A machine code manipulation library for Intel 64 and IA-32.
... - 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 -
7
Active Learning
Framework and examples for active learning with machine learning model
... experiment runner (run_experiment.py) supports a wide range of configurations, including batch sizes, dataset subsets, model selection, and data preprocessing options. 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. -
8
Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a... -
9
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). -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
-
10
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. -
11
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.
-
12
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! -
13
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). -
14
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). -
15
-
16
PredicircRNATool
A SVM-based model for prediction of circRNAs
We developed a machine learning method to computational identification of circular RNAs based on conformational and thermodynamic properties in the flanking introns. -
17
classify-20-NG-with-4-ML-Algo
Problem involves classifying 20000 messages into different 20 classes
This classification problem involves classifying 20000 messages into 20 different classes. The dataset can be found here: https://archive.ics.uci.edu/ml/datasets/Twenty+Newsgroups. Four Machine Learning algorithms; Naïve Bayes, Logistic Regression, Regularized Logistic Regression Support Vector Machine (SVM) were implemented and there training and test dataset accuracy were compared. Arguably, one of the most important aspect to solving this problem is having the appropriate data set format... -
18
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. -
19
SAMSVM
A tool for misalignment filtration on SAM-format sequences with SVM
Applying the LIBSVM, a package of support vector machine, SAMSVM was developed to correctly detect and filter the misaligned reads of SAM format. Such filtration can reduce false positives in alignment and the following variant analysis. -
20
... classification method, Support Vector Machines (SVM), is used to develop a tool to discriminate between hub and non hub proteins. Funding from Department of Information Technology,Govt. of India, (DIT/R&D/B10/15(23)2008, dated 07/09/2010), is acknowledged.
-
21
Preference Learning Toolbox
Open source toolbox to create preference models
Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations. As the number of generated data of ordinal nature such as ranks and subjective ratings is increasing, the importance and role of the PL field becomes central within machine learning research and practice. This SourceForge project provides an open source preference learning toolbox (PLT) that supports the key data modelling phases incorporating various popular data preprocessing,... -
22
ConvNetJS
Deep learning in Javascript to train convolutional neural networks
ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify... -
23
Rcnn
R-CNN: Regions with Convolutional Neural Network Features
This repository contains the original MATLAB implementation of R-CNN (Regions with Convolutional Neural Networks), a pioneering deep learning-based object detection framework. Developed by Ross Girshick, R-CNN combines region proposals with convolutional neural networks to detect objects in images. It was one of the first approaches to significantly improve performance on object detection benchmarks like PASCAL VOC. -
24
ML Toolbox
Matlab toolbox for Machine Learning
Matlab toolbox designed to simplify training, validation and testing process for multiple probabilistic models, including SVM, HMM and CRF. The toolbox is designed to work with Matlab Distributed Engine, allowing a distributed training of the probabilistic models. -
25
PiSvM is a parallel Support Vector Machine (SVM) implementation. It supports C-SVC, nu-SVC, epsilon-SVR and nu-SVR and has a command-line interface similar to the popular LibSVM package.
