一个开源的MATLAB模式识别算法
An Open Source Pattern Recognition Toolbox for MATLAB
摘要：
模式识别和机器学习正在成为广泛应用中算法的组成部分。机器学习的不同算法和方法包括性能和计算之间的不同权衡，因此在算法开发期间，通常需要探索各种不同的方法来完成给定任务。具有跨多种模式识别技术的统一框架的工具箱使算法开发人员能够在部署之前快速评估不同的选择。MATLAB是一种广泛使用的算法开发和原型开发环境，尽管目前有几个用于模式识别的MATLAB工具箱，但这些工具箱要么不完整，要么价格昂贵，要么受到限制。在这项工作中，我们描述了一个MATLAB工具箱的模式识别和机器学习被称为PRT（模式识别），许可下的MIT许可证。PRT包括许多流行的数据预处理技术，监督学习，聚类，回归和特征选择，以及使用简单，统一的语法组合这些组件的方法。可以使用交叉验证和各种评分指标来评估所得到的算法，以确保部署算法时的鲁棒性能。本文介绍了PRT的概述以及在Fisher's Iris数据集上的使用示例。
英文摘要：
Pattern recognition and machine learning are becoming integral parts of algorithms in a wide range of applications. Different algorithms and approaches for machine learning include different tradeoffs between performance and computation, so during algorithm development it is often necessary to explore a variety of different approaches to a given task. A toolbox with a unified framework across multiple pattern recognition techniques enables algorithm developers the ability to rapidly evaluate different choices prior to deployment. MATLAB is a widely used environment for algorithm development and prototyping, and although several MATLAB toolboxes for pattern recognition are currently available these are either incomplete, expensive, or restrictively licensed. In this work we describe a MATLAB toolbox for pattern recognition and machine learning known as the PRT (Pattern Recognition Toolbox), licensed under the permissive MIT license. The PRT includes many popular techniques for data preprocessing, supervised learning, clustering, regression and feature selection, as well as a methodology for combining these components using a simple, uniform syntax. The resulting algorithms can be evaluated using cross-validation and a variety of scoring metrics to ensure robust performance when the algorithm is deployed. This paper presents an overview of the PRT as well as an example of usage on Fisher's Iris dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
