A-Simple-Multi-Class-Boosting-Framework-with-Theoretical-Guarantees-and-Empirical-Proficiency

Boosting framework

A framework implementing a boosting approach for multi-class classification problems with theoretical guarantees and empirical proficiency.

Implementation of an artical

GitHub
0 stars
1 watching
0 forks
Language: HTML
last commit: almost 7 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

Repository Description Stars
anitan0925/resfgb An implementation of functional gradient boosting based on residual network perception for non-linear classification problems. 28
younghjung/onlinemlrboostingwithvfdt An implementation of online multi-label ranking boosting using VFDT as weak learners 4
charliermarsh/online_boosting A suite of algorithms and weak learners for the online learning setting in machine learning 65
raphaelcampos/stacking-bagged-boosted-forests This project presents a novel approach to classification using Random Forests and stacking techniques 6
sjsingh91/ib-cnn A library implementing a learning algorithm for improving classification accuracy with incremental updates and ensemble methods using neural networks 2
typelift/basis An exploration of pure declarative programming in Swift, with the aim of explaining complex algebraic structures without relying on specific functional languages. 316
gianlucabertani/machinelearning A machine learning framework for native code on Macs with support for neural networks and natural language processing. 37
wenkehuang/fccl A framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning 97
federatedai/eggroll A framework for distributed machine learning 242
alejandro-isaza/caffe A C++ implementation of a deep learning framework designed for speed and modularity. 59
lift/framework A comprehensive web framework that enables developers to build fast, secure, and scalable applications with real-time capabilities using the Scala programming language. 1,267
harshakokel/kigb An open-source software framework that integrates human advice into gradient boosting decision trees for improved performance in machine learning tasks. 8
chengyangfu/caffe A fast and modular deep learning framework for computer vision tasks. 169
nnikolaou/cost-sensitive-boosting-tutorial Provides tools and methods for handling asymmetric classification problems in machine learning 26
boostercloud/booster An event-driven framework for building scalable microservices with CQRS and Event Sourcing patterns in mind 421