🤖 AI Summary
To address the low training efficiency and excessive computational cost of traditional machine learning algorithms on large-scale datasets, this paper proposes a high-efficiency training framework grounded in mathematical reformulation and numerical optimization. By rigorously rederiving and restructuring the core optimization procedures of classical algorithms—including logistic regression, support vector machines (SVM), and linear regression—the framework eliminates redundant computations and integrates sparse acceleration with low-rank approximations, all while preserving theoretical accuracy. Experiments demonstrate significant speedups: up to 4× faster than scikit-learn on multivariate linear regression, logistic regression, and SVM tasks; and as high as 800× acceleration for SVM on large-scale datasets, with zero loss in predictive accuracy. The framework unifies support for regression, classification, clustering, and dimensionality reduction, offering both generality and scalability. It establishes a new paradigm for large-scale machine learning that simultaneously ensures high precision and high computational efficiency.
📝 Abstract
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.