Connectionist learning of weights for k-NN retrieval

2011 ◽  
Author(s):  
Ananda Rabi Dhar
Keyword(s):  
Connectionist Learning

scholarly journals Connectionist learning procedures

1989 ◽  
Vol 40 (1-3) ◽  
pp. 185-234 ◽  
Author(s):  
Geoffrey E. Hinton
Keyword(s):  
Connectionist Learning

Canadian Weather Analysis Using Connectionist Learning Paradigms

2003 ◽  
pp. 21-32 ◽  
Author(s):  
Imran Maqsood ◽  
Muhammad Riaz Khan ◽  
Ajith Abraham
Keyword(s):  
Connectionist Learning

Fast connectionist learning: words and case

1989 ◽  
Vol 3 (1) ◽  
pp. 33-47 ◽  
Author(s):  
N. E. Sharkey
Keyword(s):  
Connectionist Learning

A neural network-based machine learning approach for supporting synthesis

1994 ◽  
Vol 8 (2) ◽  
pp. 143-161 ◽  
Author(s):  
Nenad Ivezic ◽  
James H. Garrett
Keyword(s):  
Machine Learning ◽  
Preliminary Test ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Estimation Function ◽  
Design Property ◽  
Design Requirements ◽  
And Behavior ◽  
Future Work ◽  
Connectionist Learning

AbstractThe goal of machine learning for artifact synthesis is the acquisition of the relationships among form, function, and behavior properties that can be used to determine more directly form attributes that satisfy design requirements. The proposed approach to synthesis knowledge acquisition and use (SKAU) described in this paper, called NETSYN, creates a function to estimate the probability of each possible value of each design property being used in a given design context. NETSYN uses a connectionist learning approach to acquire and represent this probability estimation function and exhibits good performance when tested on an artificial design problem. This paper presents the NETSYN approach for SKAU, a preliminary test of its capability, and a discussion of issues that need to be addressed in future work.

Sign in / Sign up

Export Citation Format

Share Document