From: "William " <>
Newsgroups: comp.soft-sys.matlab
Subject: Performance function with pattern recognition in neural networks
Date: Wed, 27 Mar 2013 19:17:22 +0000 (UTC)
Organization: AMEC
Lines: 7
Message-ID: <kivgk2$i7$>
Reply-To: "William " <>
Content-Type: text/plain; charset=UTF-8; format=flowed
Content-Transfer-Encoding: 8bit
X-Trace: 1364411842 583 (27 Mar 2013 19:17:22 GMT)
NNTP-Posting-Date: Wed, 27 Mar 2013 19:17:22 +0000 (UTC)
X-Newsreader: MATLAB Central Newsreader 3218151
Xref: comp.soft-sys.matlab:792116


A question for anyone who might know or have an opinion.

I have set up a neural network to perform a pattern recognition (or classification) but I have found I am getting way too many false negatives compared to what I might actually get with say a Support Vector Machine set up.  One possibility I am thinking is that the SVM set up can have harsh penalties for incorrect classifications.  So, with this in mind, is there a "best" performance function for pattern recognition with neural networks??  Or am I best to say use use some function on the distance from the hyperplane (or similar)??