From dfd9d367447fc68bb440f2f03ca7bf9920b43b39 Mon Sep 17 00:00:00 2001 From: Jim Martens Date: Thu, 7 Mar 2019 15:32:22 +0100 Subject: [PATCH] Added MNIST reference Signed-off-by: Jim Martens --- ma.bib | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/ma.bib b/ma.bib index 3deef8d..a49e0a9 100644 --- a/ma.bib +++ b/ma.bib @@ -623,4 +623,20 @@ to construct explicit models for non-normal classes. Application includes infere timestamp = {2019.03.06}, } +@Article{Lecun1998, + author = {Y. Lecun and L. Bottou and Y. Bengio and P. Haffner}, + title = {Gradient-based learning applied to document recognition}, + journal = {Proceedings of the {IEEE}}, + year = {1998}, + volume = {86}, + number = {11}, + pages = {2278--2324}, + doi = {10.1109/5.726791}, + abstract = {Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.}, + file = {:/home/jim/Documents/Studium/MA/Literatur/40_MNIST.pdf:PDF}, + owner = {jim}, + publisher = {Institute of Electrical and Electronics Engineers ({IEEE})}, + timestamp = {2019.03.07}, +} + @Comment{jabref-meta: databaseType:biblatex;}