Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.12774 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918142098276352 |
|---|---|
| author | Çalışkan, Halil Hüseyin Koruk, Talha |
| author_facet | Çalışkan, Halil Hüseyin Koruk, Talha |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12774 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | EmbeddedML: A New Optimized and Fast Machine Learning Library Çalışkan, Halil Hüseyin Koruk, Talha Machine Learning Artificial Intelligence 68T05, 26A06, 15Axx I.2.6; D.1.3; I.5.1 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. |
| title | EmbeddedML: A New Optimized and Fast Machine Learning Library |
| topic | Machine Learning Artificial Intelligence 68T05, 26A06, 15Axx I.2.6; D.1.3; I.5.1 |
| url | https://arxiv.org/abs/2509.12774 |