This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
On the Asymptotic Improvement in the Out- come of Supervised Learning Provided by Additional Nonsupervised Learning
November 1970 (vol. 19 no. 11)
pp. 1055-1063
This paper treats an aspect of the learning or estimation phase of statistical pattern recognition (and adaptive statistical decision making in general). Simple mathematical expressions are derived for the improvement in supervised learning provided by additional nonsupervised learning when the number of learning samples is large so that asymptotic approximations are appropriate. The paper consists largely of the examination of a specific example, but, as is briefly discussed, the same procedure can be applied to other parametric problems and generalization to nonparametric problems seems possible. The example treated has the additional interesting aspect that the data does not have structure that would enable the machine to learn in the nonsupervised mode alone; but the additional nonsupervised learning can provide substantial improvement over the results obtainable by supervised learning alone. A second purpose of the paper is to suggest that a new fruitful area of research is the analytical study of the possible benefits of combining supervised and nonsupervised learning.
Index Terms:
Adaptive statistical decision making, adaptive systems, data clustering, statistical estimation, statistical pattern recognition, supervised and nonsupervised machine learning.
Citation:
D.B. Cooper, J.H. Freeman, "On the Asymptotic Improvement in the Out- come of Supervised Learning Provided by Additional Nonsupervised Learning," IEEE Transactions on Computers, vol. 19, no. 11, pp. 1055-1063, Nov. 1970, doi:10.1109/T-C.1970.222832
Usage of this product signifies your acceptance of the Terms of Use.