Description: Neural network ensembles are an advanced technique in the field of machine learning that combines multiple neural networks to tackle complex problems and enhance the robustness and accuracy of multimodal predictions. These networks can process different types of data, such as text, images, and audio, allowing the model to learn more effectively by integrating information from various sources. The main feature of neural network ensembles is their ability to merge the outputs of several networks, each specialized in a specific type of data or task, resulting in a more comprehensive and versatile model. This combination not only improves prediction accuracy but also helps mitigate the risk of overfitting, as different networks can compensate for each other’s weaknesses. In a world where data is increasingly multimodal, the significance of neural network ensembles lies in their ability to provide more integrated and effective solutions, facilitating the interpretation and analysis of complex information. Their use extends to various applications across different domains, from computer vision to natural language processing, making them an essential tool in the research and development of artificial intelligence.
