A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
- gbdt
- gbm
- machine-learning
- data-mining
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A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.
- machine-learning
- decision-trees
- gradient-boosting
- gbm
C++ implementation of the Python Numpy library
- python
- numpy
- c-plus-plus
- numerical-analysis
A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
- robotics
- dynamics
- analytical-derivatives
- kinematics
A Python package for manipulating 2-dimensional tabular data structures
- python
- data-analysis
- data-structure
- performance
A collection of GICP-based fast point cloud registration algorithms
- point-cloud
- pcl
- gicp
- icp
A c++ toolbox of locality-sensitive hashing (LSH), provides several popular LSH algorithms, also support python and matlab.
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.
- svm
- regression
- classification
- machine-learning