A Mixed-Type Non-Parametric Learning Machine without a Teacher View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1971

AUTHORS

Masamichi Shimura

ABSTRACT

In this paper, we consider the design of a non-parametric learning machine without a teacher. Most pattern recognition problems may be categorized as parametric or non-parametric on the basis of knowledge that we have concerning the conditional densities of the input patterns. Problems in which the densities are completely unknown are called non-parametric. In addition, the learning machine can be further classified into two types. One is a supervised machine, that is, a machine with an external teacher. In this case, the teacher gives the information regarding the category to which the input pattern belongs and the information regarding the correctness of the machine’s action. The second type is an unsupervised machine. More... »

PAGES

42-55

Book

TITLE

Pattern Recognition and Machine Learning

ISBN

978-1-4615-7568-9
978-1-4615-7566-5

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4615-7566-5_4

DOI

http://dx.doi.org/10.1007/978-1-4615-7566-5_4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1038672960


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