Vapnik Vladimir, Vashist Akshay
NEC Labs America, 4 Independence Way, Princeton, NJ 08540, United States.
Neural Netw. 2009 Jul-Aug;22(5-6):544-57. doi: 10.1016/j.neunet.2009.06.042. Epub 2009 Jul 3.
In the Afterword to the second edition of the book "Estimation of Dependences Based on Empirical Data" by V. Vapnik, an advanced learning paradigm called Learning Using Hidden Information (LUHI) was introduced. This Afterword also suggested an extension of the SVM method (the so called SVM(gamma)+ method) to implement algorithms which address the LUHI paradigm (Vapnik, 1982-2006, Sections 2.4.2 and 2.5.3 of the Afterword). See also (Vapnik, Vashist, & Pavlovitch, 2008, 2009) for further development of the algorithms. In contrast to the existing machine learning paradigm where a teacher does not play an important role, the advanced learning paradigm considers some elements of human teaching. In the new paradigm along with examples, a teacher can provide students with hidden information that exists in explanations, comments, comparisons, and so on. This paper discusses details of the new paradigm and corresponding algorithms, introduces some new algorithms, considers several specific forms of privileged information, demonstrates superiority of the new learning paradigm over the classical learning paradigm when solving practical problems, and discusses general questions related to the new ideas.
在弗拉基米尔·瓦普尼克所著的《基于经验数据的依赖关系估计》第二版后记中，引入了一种名为利用隐藏信息学习（LUHI）的先进学习范式。该后记还建议对支持向量机方法（即所谓的SVM(γ)+方法）进行扩展，以实现处理LUHI范式的算法（瓦普尼克，1982 - 2006年，后记第2.4.2节和2.5.3节）。有关算法的进一步发展，另见（瓦普尼克、瓦希斯特和帕夫洛维奇，2008年、2009年）。与现有机器学习范式中教师不发挥重要作用不同，先进学习范式考虑了人类教学的一些要素。在新范式中，除了示例之外，教师还可以向学生提供存在于解释、评论、比较等中的隐藏信息。本文讨论了新范式及相应算法的细节，介绍了一些新算法，考虑了几种特权信息的特定形式，展示了新学习范式在解决实际问题时相对于经典学习范式的优越性，并讨论了与新思想相关的一般性问题。
