TensorFlow implementation of an arbitrary order Factorization Machine
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.
Relevance Vector Machine implementation using the scikit-learn API.
The main features of jamesritchie/scikit-rvm are: Kernel Methods.
Open-source alternatives to jamesritchie/scikit-rvm include: coreylynch/pyfm — Factorization machines in python. geffy/tffm — TensorFlow implementation of an arbitrary order Factorization Machine. ibayer/fastfm — fastFM: A Library for Factorization Machines. 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.
