DSpace Repository

Linear Separation and Learning

Show simple item record

dc.creator Minsky, Marvin
dc.creator Papert, Seymour
dc.date 2004-10-04T14:44:00Z
dc.date 2004-10-04T14:44:00Z
dc.date 1968-10-01
dc.date.accessioned 2013-10-09T02:43:43Z
dc.date.available 2013-10-09T02:43:43Z
dc.date.issued 2013-10-09
dc.identifier AIM-167
dc.identifier http://hdl.handle.net/1721.1/6170
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description This is a reprint of page proofs of Chapter 12 of Perceptrons, M. Minsky and S. Papert, MIT Press 1968, (we hope). It replaces A.I. Memo No. 156 dated March 1968. The perceptron and convergence theorems of Chapter 11 are related to many other procedures that are studied in an extensive and disorderly literature under such titles as LEARNING MACHINES, MODELS OF LEARNING, INFORMATION RETRIEVAL, STATISTICAL DECISION THEORY, PATTERN RECOGNITION and many more. In this chapter we will study a few of these to indicate points of contact with the perception and to revel deep differences. We can give neither a fully rigorous account not a unifying theory of these topics: this would go as far beyond our knowledge as beyond the scope of this book. The chapter is written more in the spirit of inciting students to research than to offering solutions to problems.
dc.format 14956944 bytes
dc.format 1208423 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-167
dc.title Linear Separation and Learning


Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search DSpace


Advanced Search

Browse

My Account