Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910913737523200 |
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| author | Kiruluta, Andrew Lemos, Andreas |
| author_facet | Kiruluta, Andrew Lemos, Andreas |
| contents | This paper presents a novel hybrid approach that integrates linear programming (LP) within the loss function of an unsupervised machine learning model. By leveraging the strengths of both optimization techniques and machine learning, this method introduces a robust framework for solving complex optimization problems where traditional methods may fall short. The proposed approach encapsulates the constraints and objectives of a linear programming problem directly into the loss function, guiding the learning process to adhere to these constraints while optimizing the desired outcomes. This technique not only preserves the interpretability of linear programming but also benefits from the flexibility and adaptability of machine learning, making it particularly well-suited for unsupervised or semi-supervised learning scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09967 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique Kiruluta, Andrew Lemos, Andreas Machine Learning Artificial Intelligence Optimization and Control This paper presents a novel hybrid approach that integrates linear programming (LP) within the loss function of an unsupervised machine learning model. By leveraging the strengths of both optimization techniques and machine learning, this method introduces a robust framework for solving complex optimization problems where traditional methods may fall short. The proposed approach encapsulates the constraints and objectives of a linear programming problem directly into the loss function, guiding the learning process to adhere to these constraints while optimizing the desired outcomes. This technique not only preserves the interpretability of linear programming but also benefits from the flexibility and adaptability of machine learning, making it particularly well-suited for unsupervised or semi-supervised learning scenarios. |
| title | Unsupervised Machine Learning Hybrid Approach Integrating Linear Programming in Loss Function: A Robust Optimization Technique |
| topic | Machine Learning Artificial Intelligence Optimization and Control |
| url | https://arxiv.org/abs/2408.09967 |