Search Results for "svm"
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Showing 60 open source projects for "svm"
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HubSpot is an AI-powered customer platform with all the software, integrations, and resources you need to connect your marketing, sales, and customer service. HubSpot's connected platform enables you to grow your business faster by focusing on what matters most: your customers.
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ChangeGear provides IT staff with the functions required to manage everything from ticketing to incident, change and asset management and more. ChangeGear includes a virtual agent, self-service portals and AI-based features to support analyst and end user productivity.
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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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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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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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PMG is a low-code software platform that allows users to configure automation solutions and business applications to drive digital transformation initiatives. From streamlining business processes through automation, to integrating existing systems and filling in point solution functionality gaps, to delivering a collaborative workspace and unified user experience – PMG’s low-code platform does it all without coding. Business users as well as IT resources are empowered to configure, deploy, and maintain solutions that meet their company’s specific needs.
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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 -
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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... -
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This project includes a short video for KDD 2018 paper "Isolation Kernel and Its Effect on SVM", and two implements of Isolation Kernel, one for small or medium size data with higher accuracy, the other for large data with higher efficiency and less memory requirement.
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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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Innago is a free and easy-to-use property management solution. Whether you have 1 unit or 1000, student housing, or commercial properties, Innago is built for you. Our software is designed to save you time and money, so you can spend more time doing the things that matter most.
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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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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. -
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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... -
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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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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. -
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... 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, feature... -
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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... -
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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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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.
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svmradiusmargin
code for convex formulation of radius-margin based SVM
Matlab code for the novel algorithms presented in the paper Convex formulation for radius-margin based Support Vector Machines -
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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 classification problems with millions of high... -
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... 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]