Gradyan artırmalı makinelerle doğrusal olmayan regresyon için çoklu sekanslı yeni bir optimizasyon çerçevesi

Gradient Boosting Machines (GBMs) consistently achieve state-of-the-art performance on a wide range of machine learning applications, particularly for problems tabular data. However, their underlying optimization mechanism largely relies on a greedy form of functional gradient descent. This classical approach, while effective, can be path-dependent under noise and prone to locally optimal updates that yield globally suboptimal models. To address these fundamental limitations, we propose a multi-sequence framework that integrates principles from modern optimization directly into the gradient boosting process.Unlike prior work that attempts direct adaptations of vector-based optimizers such as Nesterov's accelerated gradient, our method employs a decoupled architecture inspired by stochastic primal averaging (SPA). This architecture provides a stable foundation upon which we build a series of adaptive target updaters that translate the mechanics of modern optimizers such as Adam into principled learning signals for weak learners. We provide a theoretical analysis, including a linear convergence proof for our base model under standard smoothness and strong convexity assumptions. Our experimental results demonstrate significant improvements in stability and accuracy over LightGBM and other baselines, proving our framework's ability to offer a new level of control over the optimization trajectory while preserving the simplicity and modularity that make GBMs practical, leading to more robust and powerful gradient boosting models.