การสร้างคุณลักษณะแบบเพิ่มสำหรับการเรียนรู้เชิงลึกโดยใช้ตัวเข้ารหัสอัตโนมัติ - มงคล อุดมมิตรรัก (จุฬาลงกรณ์มหาวิทยาลัย, 2013)
In this thesis we proposed incremental feature construction for deep learning using an auto-encoder which is a novel alternative method for learning features of each layer of a neural. In conventional auto-encoder training, all feature units are simultaneously constructed at the beginning. Our method incrementally constructs features by adding primitive/simple features first and then gradually learns finer/more complicated features. We run experiments on the MNIST and CIFAR-10 data sets. The experimental results show that feature learning by combine sparse auto-encoder with denoising auto-encoders using our incremental feature construction provides better accuracy than ones using the conventional feature construction.