arXiv:2603.15299v1 Announce Type: new 
Abstract: We propose a novel approach which exploits chaos to enhance classification accuracy. Specifically, the available data that need to be classified are treated as vectors that are first lifted into a higher-dimensional space and then used as initial conditions for the evolution of a chaotic dynamical system for a prescribed temporal interval. The evolved state of the dynamical system is then fed to a trainable softmax classifier which outputs the probabilities of the various classes. As proof-of-concept, we use samples of randomly perturbed orthogonal vectors of moderate dimension (2 to 20), with a corresponding number of classes equal to the vector dimension, and show how our approach can both significantly accelerate the training process and improve the classification accuracy compared to a standard softmax classifier which operates on the original vectors, as well as a softmax classifier which only lifts the vectors to a higher-dimensional space without evolving them. We also provide an explanation for the improved performance of the chaos-enhanced classifier.

تم اقتراح نهج جديد لتحسين دقة التصنيف من خلال استغلال الفوضى، حيث يتم التعامل مع البيانات كمتجهات يتم رفعها إلى فضاء ذي أبعاد أعلى وتستخدم كظروف أولية لنظام ديناميكي فوضوي. هذه الطريقة تسرع بشكل كبير من عملية التدريب وتحسن الدقة مقارنة بال classifiers القياسية.

Se ha propuesto un enfoque novedoso para mejorar la precisión de clasificación aprovechando el caos, donde los datos se tratan como vectores elevados a un espacio de mayor dimensión y se utilizan como condiciones iniciales para un sistema dinámico caótico. Este método acelera significativamente el entrenamiento y mejora la precisión en comparación con los clasificadores estándar.

Une nouvelle approche a été proposée pour améliorer la précision de classification en exploitant le chaos, où les données sont traitées comme des vecteurs élevés dans un espace de dimension supérieure et utilisées comme conditions initiales pour un système dynamique chaotique. Cette méthode accélère considérablement l'entraînement et améliore la précision par rapport aux classificateurs standard.

A novel approach has been proposed to enhance classification accuracy by leveraging chaos, where data is treated as vectors lifted into a higher-dimensional space and used as initial conditions for a chaotic dynamical system. This method significantly accelerates training and improves accuracy compared to standard classifiers.