MESSAGE
| DATE | 2016-10-05 |
| FROM | ruben safir
|
| SUBJECT | Subject: [Learn] Fwd: Re: Check List of Texts to learn Cladistics and
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From learn-bounces-at-nylxs.com Wed Oct 5 00:39:25 2016
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From: ruben safir
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Subject: [Learn] Fwd: Re: Check List of Texts to learn Cladistics and
Computational Math
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-------- Forwarded Message --------
Subject: Re: Check List of Texts to learn Cladistics and Computational Math
Date: Tue, 4 Oct 2016 18:13:07 -0700
From: John Harshman
Newsgroups: sci.bio.paleontology
References:
On 10/4/16 6:02 PM, ruben safir wrote:
> On 10/04/2016 08:51 PM, John Harshman wrote:
>> On 10/4/16 5:33 PM, ruben safir wrote:
>>> On 10/03/2016 11:13 PM, John Harshman wrote:
>>>>> I don't know that with certainty. Do you have a specific suggestion?
>>>>>
>>>> I would have to know your intentions first.
>>>
>>> all men are dogs. You know my intentions.
>>>
>> I have no idea how that was supposed to help me.
>
> my intention is to learn how researches currently quantify and create
> the data they get from fossils to create cladistic, phylogenic trees,
> how they create those trees, and to use that knowledge in order to try
> to analyze their current methods from the prespective of machine
> learning and AI applications which can improve it.
Then in order to talk about improving methods, you need to find a group
for which you are willing to say that the true phylogeny is known and
then compare various methods of analysis to see which most reliably
produce that true phylogeny. Tyrannosaurids might not be the best for
that purpose. Within archosaurs, you might try looking at the
relationships of gharials to other living crocodylians, for which I
think the molecular data are conclusive but the morphological analyses
mostly systematically in error. Similarly, the position of whales within
artiodactyls has been difficult to morphology to deal with but easy for
molecular data.
Or you could try another route: simulated data on a known tree,, or
manufactured data like the caminalcules.
A concentration on character coding might be interesting; perhaps
machine learning could help with it. I don't see how machine learning
could help the analysis step. Though there is a phylogenetic program,
GARLI, that uses a genetic algorithm; perhaps that's something like you
propose?
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