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: over 8 years ago
Linked from 1 awesome list


Backlinks from these awesome lists:

Related projects:

RepositoryDescriptionStars
anitan0925/resfgbAn implementation of functional gradient boosting based on residual network perception for non-linear classification problems.28
younghjung/onlinemlrboostingwithvfdtAn implementation of online multi-label ranking boosting using VFDT as weak learners4
charliermarsh/online_boostingA suite of algorithms and weak learners for the online learning setting in machine learning65
raphaelcampos/stacking-bagged-boosted-forestsThis project presents a novel approach to classification using Random Forests and stacking techniques6
sjsingh91/ib-cnnA library implementing a learning algorithm for improving classification accuracy with incremental updates and ensemble methods using neural networks2
typelift/basisAn exploration of pure declarative programming in Swift, with the aim of explaining complex algebraic structures without relying on specific functional languages.316
gianlucabertani/machinelearningA machine learning framework for native code on Macs with support for neural networks and natural language processing.37
wenkehuang/fcclA framework for tackling heterogeneity and catastrophic forgetting in federated learning by leveraging cross-correlation and similarity learning97
federatedai/eggrollA framework for distributed machine learning242
alejandro-isaza/caffeA C++ implementation of a deep learning framework designed for speed and modularity.59
lift/frameworkA 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/kigbAn open-source software framework that integrates human advice into gradient boosting decision trees for improved performance in machine learning tasks.8
chengyangfu/caffeA fast and modular deep learning framework for computer vision tasks.169
nnikolaou/cost-sensitive-boosting-tutorialProvides tools and methods for handling asymmetric classification problems in machine learning26
boostercloud/boosterAn event-driven framework for building scalable microservices with CQRS and Event Sourcing patterns in mind421