Search Results for "svm"
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Showing 52 open source projects for "svm"
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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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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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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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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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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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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. -
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...Our prediction results show that meaningful amino acid features can produce signature features for differentiating hubs from non-hubs. The classical 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.
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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,... -
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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. -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
...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 and train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. The library allows you to formulate and solve Neural Networks in Javascript. If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. ... -
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BudgetedSVM
BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification
We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of high-dimensional examples within minutes on a regular personal computer. ... -
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.... --------------------------------------------------------------------------- For Self-Learning: java -jar -Xms1700m SelfLearner.jar [trainFile] [testFile] [labelFile] [unlabeledFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFile] For Co-Training: java -jar -Xms2500m CoTraining.jar [trainFile-Side1] [testFile-Side1] [labelFile-Side1] [unlabeledFile-Side1] [trainFile-Side2] [testFile-Side2] [labelFile-Side2] [unlabeledFile-Side2] [MappingFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFileSide1] [ClassifierModelFileSide2]
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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. -
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VOC-DPM
Object detection system using deformable part models (DPMs)
The VOC-DPM repository is an implementation of an object detection system built on deformable part models (DPMs) and latent SVM learning, specifically packaged as “voc-release5.” It is the companion code for Ross Girshick’s dissertation, and extends earlier work on discriminatively trained DPMs. The system supports a grammar-based representation for object models, allowing structures such as mixtures and hierarchies to represent parts and whole objects. It implements both latent SVM training (where part assignments are treated as latent variables) and weak-label structural SVM (WL-SSVM) for learning from partially labeled data. ...
