Forward Layer-wise Learning Of Convolutional Neural Networks Through Separation Index Maximizing

This paper proposes a forward layer-wise learning algorithm for CNNs in classification problems, utilizing the Separation Index (SI) as a supervised complexity measure. The algorithm outperforms state-of-the-art methods in accuracy and time complexity on image and text classification tasks.