论文标题
通过训练期间的变化功能对模型权重的有效学习
Efficient Learning of Model Weights via Changing Features During Training
论文作者
论文摘要
在本文中，我们提出了一个机器学习模型，该模型在训练过程中动态改变了功能。我们的主要动机是在训练过程中以较小的内容更新模型，并将较小的描述性功能替换为大型池的新功能。主要的好处来自这样一个事实，即与我们没有从头开始训练新模型的共同实践相反，而是可以保持已经学习的权重。此过程允许扫描大型池，该池以及保持模型的复杂性会导致在同一训练时间内提高模型精度。在几种经典的机器学习场景中，包括线性回归和基于神经网络的培训，可以证明我们方法的效率。作为对信号处理的特定分析，我们已经成功地测试了数据库MNIST上的方法，以考虑单个像素和像素对的强度作为可能的特征。
In this paper, we propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool. The main benefit is coming from the fact that opposite to the common practice we do not start training a new model from the scratch, but can keep the already learned weights. This procedure allows the scan of a large feature pool which together with keeping the complexity of the model leads to an increase of the model accuracy within the same training time. The efficiency of our approach is demonstrated in several classic machine learning scenarios including linear regression and neural network-based training. As a specific analysis towards signal processing, we have successfully tested our approach on the database MNIST for digit classification considering single pixel and pixel-pairs intensities as possible features.
