How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
Relevance Vector Machine implementation using the scikit-learn API.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.
TensorFlow implementation of an arbitrary order Factorization Machine
The main features of geffy/tffm are: Kernel Methods.
Open-source alternatives to geffy/tffm include: coreylynch/pyfm — Factorization machines in python. ibayer/fastfm — fastFM: A Library for Factorization Machines. jamesritchie/scikit-rvm — Relevance Vector Machine implementation using the scikit-learn API. liquidsvm/liquidsvm — Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning… xtra-computing/thundersvm — ThunderSVM: A Fast SVM Library on GPUs and CPUs.
