论文标题
opfython：python启发的最佳路径森林分类器
OPFython: A Python-Inspired Optimum-Path Forest Classifier
论文作者
论文摘要
在过去的几年中，机器学习技术一直是最重要的，在各种任务中应用，例如分类，对象识别，人识别和图像分段。然而，常规分类算法，例如逻辑回归，决策树和贝叶斯分类器，可能缺乏复杂性和多样性，在处理现实世界数据时不合适。被证明是一种被称为“最佳路径森林”的最新图形启发的分类器，已被证明是一种最先进的技术，可与支持向量机器相当，甚至在某些任务中超过它。本文提出了一个基于Python的最佳路径森林框架，称为Opfython，其所有功能和类都基于原始C语言实现。此外，由于Opfython是一个基于Python的库，因此它提供了一个比C语言更友好的环境和更快的原型工作空间。
Machine learning techniques have been paramount throughout the last years, being applied in a wide range of tasks, such as classification, object recognition, person identification, and image segmentation. Nevertheless, conventional classification algorithms, e.g., Logistic Regression, Decision Trees, and Bayesian classifiers, might lack complexity and diversity, not suitable when dealing with real-world data. A recent graph-inspired classifier, known as the Optimum-Path Forest, has proven to be a state-of-the-art technique, comparable to Support Vector Machines and even surpassing it in some tasks. This paper proposes a Python-based Optimum-Path Forest framework, denoted as OPFython, where all of its functions and classes are based upon the original C language implementation. Additionally, as OPFython is a Python-based library, it provides a more friendly environment and a faster prototyping workspace than the C language.
