Ruby language bindings for LIBSVM
The main features of febeling/rb-libsvm are: Machine Learning, Machine Learning Libraries, Statistical and Predictive Models, Machine Learning Frameworks.
Open-source alternatives to febeling/rb-libsvm include: catboost/catboost — CatBoost is a gradient boosting machine learning library used to train decision tree ensembles for regression,… davisking/dlib — dlib is a C++ machine learning toolkit and data analysis framework. It provides a collection of algorithms and… djcp/linnaeus — Linnaeus is a redis-backed naive Bayesian classification system. Please see the rdoc for more information. Ruby 1.9 is… cjlin1/libsvm — This project is a support vector machine library implemented in C, providing an engine for classification and… deeplearning4j/deeplearning4j — Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph… ealdent/lda-ruby — This wrapper is based on C-code by David M. Blei. In a nutshell, it can be used to automatically cluster documents…
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This project is a support vector machine library implemented in C, providing an engine for classification and regression tasks. It functions as a machine learning kernel library and a statistical model validator used to categorize data points and predict continuous numerical values. The library allows for the definition of custom kernel functions to calculate similarity between data points in specialized datasets. It also includes tools for probabilistic modeling, such as estimating class membership, data density, and distribution boundaries. Broad capabilities cover model training for multi
Deeplearning4j is a JVM-based deep learning framework and tensor computing library. It provides a computational graph engine for defining and executing deep learning workflows and mathematical operations within the Java Virtual Machine. The project includes a dedicated importer for loading and running pretrained models exported from Keras, TensorFlow, and ONNX formats. Its tensor computing capabilities are driven by a modular native C++ math core to execute high-performance linear algebra operations. The framework covers neural network training, deep learning model inference, and the constru