Description: Hybrid models are approaches that combine different techniques and methodologies to improve performance in various applications. In the context of artificial intelligence (AI), these models integrate machine learning algorithms, neural networks, and statistical methods, allowing for greater flexibility and accuracy in decision-making. The main characteristic of hybrid models is their ability to leverage the strengths of each technique while minimizing their weaknesses. For example, neural networks can be used for processing unstructured data, such as images or text, while supervised learning algorithms are applied for classification tasks. This synergy not only enhances the effectiveness of the models but also enables the tackling of complex problems that require a multifaceted approach. In various technological domains, hybrid models are particularly relevant as they allow for efficient AI tasks, optimizing the use of limited resources. In anomaly detection, these models can identify unusual patterns in large volumes of data, which is crucial in areas such as cybersecurity and industrial monitoring. In summary, hybrid models represent a significant evolution in the field of AI, offering more robust and adaptive solutions to contemporary challenges.
