期刊文献+

深度学习技术及其在肿瘤分类中的应用 被引量:4

Deep Learning Technology and Its Application in Tumor Classification
下载PDF
导出
摘要 深度学习是机器学习领域一个新兴的研究方向,吸引了工业界和学术界的广泛关注。该文从深度学习的历史渊源谈起,描述了几种主要的深度学习架构,介绍了深度学习在肿瘤分类中的一些应用,提出了目前肿瘤分类研究存在的问题。 Deep learning is an emerging field of machine learning research, which attracts wide attention in industry and academia. This paper talks about the history of deep learning, describes several major deep learning architectures, and in- troduces some applications of deep learning in tumor classification. At last, the existing problems of tumor classification are suggested.
出处 《智能计算机与应用》 2014年第6期17-19,共3页 Intelligent Computer and Applications
基金 国家自然科学基金(61373057) 教育部人文社科基金(13YJA760028)
关键词 深度学习 神经网络 肿瘤 分类 Deep Learning Neural Network Tumor Classification
  • 相关文献

参考文献1

二级参考文献14

  • 1MarkoffJ. How many computers to identify a cat?[NJ The New York Times, 2012-06-25.
  • 2MarkoffJ. Scientists see promise in deep-learning programs[NJ. The New York Times, 2012-11-23.
  • 3李彦宏.2012百度年会主题报告:相信技术的力量[R].北京:百度,2013.
  • 410 Breakthrough Technologies 2013[N]. MIT Technology Review, 2013-04-23.
  • 5Rumelhart D, Hinton G, Williams R. Learning representations by back-propagating errors[J]. Nature. 1986, 323(6088): 533-536.
  • 6Hinton G, Salakhutdinov R. Reducing the dimensionality of data with neural networks[J]. Science. 2006, 313(504). Doi: 10. 1l26/science. 1127647.
  • 7Dahl G. Yu Dong, Deng u, et a1. Context-dependent pre?trained deep neural networks for large vocabulary speech recognition[J]. IEEE Trans on Audio, Speech, and Language Processing. 2012, 20 (1): 30-42.
  • 8Jaitly N. Nguyen P, Nguyen A, et a1. Application of pretrained deep neural networks to large vocabulary speech recognition[CJ //Proc of Interspeech , Grenoble, France: International Speech Communication Association, 2012.
  • 9LeCun y, Boser B, DenkerJ S. et a1. Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, I: 541-551.
  • 10Large Scale Visual Recognition Challenge 2012 (ILSVRC2012)[OLJ.[2013-08-01J. http://www. image?net.org/challenges/LSVRC/2012/.

共引文献598

同被引文献26

引证文献4

二级引证文献64

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部