Search Results for "binary tree"

LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle...

Fast face detection, pupil/eyes localization and facial landmark points detection library in pure Go. Pigo is a pure Go face detection, pupil/eyes localization and facial landmark points detection library based on the Pixel Intensity Comparison-based Object detection paper. The reason why Pigo has been developed is because almost all of the currently existing solutions for face detection in the Go ecosystem are purely bindings to some C/C++ libraries like OpenCV or dlib, but calling a C...

...Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. ...

Java Behaviour Trees is a Java framework for easily building and running generic behaviour trees. Here, "behaviour tree" refers to the technique used to control the behaviour of characters in video games. IMPORTANT NOTE: our source code has been moved to GitHub. You will find it at: https://github.com/gaia-ucm/jbt Note however that all the binary files will still be distributed here at SourceForge.

...The TASOM has been originally introduced for adaptive and changing environments. Several versions of TASOM networks have been introduced. Some of them are capable of changing the number of neurons based on the problems at hand. Moreover, a binary tree version of the TASOM has been introduced for faster performance.