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	<title>Comments on: I&#8217;d hammer in the morning</title>
	<atom:link href="http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning/feed" rel="self" type="application/rss+xml" />
	<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning</link>
	<description>Pontification without all the gritty gravitas</description>
	<pubDate>Fri, 25 Jul 2008 08:13:26 +0000</pubDate>
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		<title>By: dsquared</title>
		<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning#comment-101</link>
		<dc:creator>dsquared</dc:creator>
		<pubDate>Sat, 01 Oct 2005 15:12:39 +0000</pubDate>
		<guid isPermaLink="false">http://williamtozier.com/slurry/?p=87#comment-101</guid>
		<description>It's what you do for when mixtures of logistic functions aren't flexible enough, isn't it?  You tend to get laughed out of the shop in econometrics when you get to this level of complexity, but that's mainly because we're usually looking for patterns that probably don't exist.</description>
		<content:encoded><![CDATA[<p>It&#8217;s what you do for when mixtures of logistic functions aren&#8217;t flexible enough, isn&#8217;t it?  You tend to get laughed out of the shop in econometrics when you get to this level of complexity, but that&#8217;s mainly because we&#8217;re usually looking for patterns that probably don&#8217;t exist.</p>
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		<title>By: Alexandre Grings</title>
		<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning#comment-98</link>
		<dc:creator>Alexandre Grings</dc:creator>
		<pubDate>Wed, 28 Sep 2005 20:04:55 +0000</pubDate>
		<guid isPermaLink="false">http://williamtozier.com/slurry/?p=87#comment-98</guid>
		<description>What the alternatives to do Symbolic Regression?
Make a web search for the term. You will see that there is no definition for the term outside GP world.
My guess is that the most of people don't even know that such a problem can be solved by a machine.
Take a look in http://www.it.lut.fi/mat/EcmiNL/ecmi35/node70.html</description>
		<content:encoded><![CDATA[<p>What the alternatives to do Symbolic Regression?<br />
Make a web search for the term. You will see that there is no definition for the term outside GP world.<br />
My guess is that the most of people don&#8217;t even know that such a problem can be solved by a machine.<br />
Take a look in <a href="http://www.it.lut.fi/mat/EcmiNL/ecmi35/node70.html" rel="nofollow">http://www.it.lut.fi/mat/EcmiNL/ecmi35/node70.html</a></p>
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		<title>By: Bill</title>
		<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning#comment-97</link>
		<dc:creator>Bill</dc:creator>
		<pubDate>Wed, 28 Sep 2005 14:32:50 +0000</pubDate>
		<guid isPermaLink="false">http://williamtozier.com/slurry/?p=87#comment-97</guid>
		<description>This is true... but you can't really make strong statements about the role of, say, qualitative relationship between mass and time variables,as you can with symbolic regression approaches.

In many cases, the customer--the person who wants and pays to see the analysis done--may not realize initially that they don't just want a classifier or a model, but also want one that provides insight to drive further first-principles modeling and experimentation. That's the shortcoming of most  NN-like and PCA-like approaches: the intermediate calculations are some function of almost all the inputs, and as a result it's nigh impossible to tease out meaningful insight.

Anyway... I'm working on it.....</description>
		<content:encoded><![CDATA[<p>This is true&#8230; but you can&#8217;t really make strong statements about the role of, say, qualitative relationship between mass and time variables,as you can with symbolic regression approaches.</p>
<p>In many cases, the customer&#8211;the person who wants and pays to see the analysis done&#8211;may not realize initially that they don&#8217;t just want a classifier or a model, but also want one that provides insight to drive further first-principles modeling and experimentation. That&#8217;s the shortcoming of most  NN-like and PCA-like approaches: the intermediate calculations are some function of almost all the inputs, and as a result it&#8217;s nigh impossible to tease out meaningful insight.</p>
<p>Anyway&#8230; I&#8217;m working on it&#8230;..</p>
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		<title>By: aleks</title>
		<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning#comment-96</link>
		<dc:creator>aleks</dc:creator>
		<pubDate>Wed, 28 Sep 2005 11:13:55 +0000</pubDate>
		<guid isPermaLink="false">http://williamtozier.com/slurry/?p=87#comment-96</guid>
		<description>Just BTW: One can convert a SVM into the form of a generalized additive model and then show it graphically: http://portal.acm.org/citation.cfm?id=1081870.1081886 The dimensionality of the visualization depends on the kernel.</description>
		<content:encoded><![CDATA[<p>Just BTW: One can convert a SVM into the form of a generalized additive model and then show it graphically: <a href="http://portal.acm.org/citation.cfm?id=1081870.1081886" rel="nofollow">http://portal.acm.org/citation.cfm?id=1081870.1081886</a> The dimensionality of the visualization depends on the kernel.</p>
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		<title>By: son1</title>
		<link>http://williamtozier.com/slurry/2005/09/23/id-hammer-in-the-morning#comment-80</link>
		<dc:creator>son1</dc:creator>
		<pubDate>Fri, 23 Sep 2005 14:21:55 +0000</pubDate>
		<guid isPermaLink="false">http://williamtozier.com/slurry/?p=87#comment-80</guid>
		<description>Just out of curiosity, do you really think that any reasonably complex Bayesian network, whose parameter-set or structure is learned from the data, is likely to be any &lt;i&gt;less&lt;/i&gt; opaque than the results of a well-designed SVM?

Never-mind the fact that the graph representation of a bayesian network often invites people to consider the edges which are drawn (when in fact, it's what &lt;i&gt;isn't&lt;/i&gt; drawn that's important, graphically) and, at the same time, to consider the edges singly (when in fact, it's a "set of edges" which form the incoming arcs to a node that are the irreducible elements in a BN), which is totally wrong.

Bleh.  (what I'm saying is, it's not about your machine-learning tool-of-choice, it's about the auxiliary tools you have available to summarize and visualize the results of whatever method you've chosen.)

But I'm intrigued... what is GP/SR?  and how is it different from a genetic algorithm?</description>
		<content:encoded><![CDATA[<p>Just out of curiosity, do you really think that any reasonably complex Bayesian network, whose parameter-set or structure is learned from the data, is likely to be any <i>less</i> opaque than the results of a well-designed SVM?</p>
<p>Never-mind the fact that the graph representation of a bayesian network often invites people to consider the edges which are drawn (when in fact, it&#8217;s what <i>isn&#8217;t</i> drawn that&#8217;s important, graphically) and, at the same time, to consider the edges singly (when in fact, it&#8217;s a &#8220;set of edges&#8221; which form the incoming arcs to a node that are the irreducible elements in a BN), which is totally wrong.</p>
<p>Bleh.  (what I&#8217;m saying is, it&#8217;s not about your machine-learning tool-of-choice, it&#8217;s about the auxiliary tools you have available to summarize and visualize the results of whatever method you&#8217;ve chosen.)</p>
<p>But I&#8217;m intrigued&#8230; what is GP/SR?  and how is it different from a genetic algorithm?</p>
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