EmbeddedML通过数学优化加速机器学习训练，大数据下快800倍。
EmbeddedML: A New Optimized and Fast Machine Learning Library
- 重构回归与分类算法数学逻辑，提升计算效率。
- 大样本SVM训练提速800倍，逻辑回归快4倍，精度不降。
- 适合需要快速训练的工程落地场景，如实时数据分析。
机器学习模型和库虽能处理不同规模数据，但在大数据集上训练缓慢。本文提出EmbeddedML，一个训练时优化且数学增强的机器学习库。该库对多元线性回归、逻辑回归和支持向量机（SVM）等算法进行数学重写，显著缩短训练时间。在回归模型中，性能与scikit-learn相当，但速度提升约数倍；在分类模型中，相较于scikit-learn实现，小数据集上SVM训练时间减少约2倍，大数据集上减少约800倍；逻辑回归训练时间减少约4倍。EmbeddedML提供回归、分类、聚类和降维算法，均经数学重构与优化，有效降低训练耗时。
原文摘要 · Abstract (English)
Machine learning models and libraries can train datasets of different sizes and perform prediction and classification operations, but machine learning models and libraries cause slow and long training times on large datasets. This article introduces EmbeddedML, a training-time-optimized and mathematically enhanced machine learning library. The speed was increased by approximately times compared to scikit-learn without any loss in terms of accuracy in regression models such as Multiple Linear Regression. Logistic Regression and Support Vector Machines (SVM) algorithms have been mathematically rewritten to reduce training time and increase accuracy in classification models. With the applied mathematical improvements, training time has been reduced by approximately 2 times for SVM on small datasets and by around 800 times on large datasets, and by approximately 4 times for Logistic Regression, compared to the scikit-learn implementation. In summary, the EmbeddedML library offers regression, classification, clustering, and dimensionality reduction algorithms that are mathematically rewritten and optimized to reduce training time.
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