A good summary: (01)
http://stackoverflow.com/questions/9979461/different-decision-tree-algorithms-with-comparison-of-complexity-or-performance (02)
On 3 Jun 2014, at 17:56, Rich Cooper wrote: (03)
> After reading C4.5, it seems nearly identical to
> ID3. What exactly is the difference?
>
> Sincerely,
> Rich Cooper
> EnglishLogicKernel.com
> Rich AT EnglishLogicKernel DOT com
> 9 4 9 \ 5 2 5 - 5 7 1 2
>
> -----Original Message-----
> From: ontolog-forum-bounces@xxxxxxxxxxxxxxxx
> [mailto:ontolog-forum-bounces@xxxxxxxxxxxxxxxx] On
> Behalf Of Jack Park
> Sent: Tuesday, June 03, 2014 5:11 PM
> To: [ontolog-forum]
> Subject: Re: [ontolog-forum] Toward Human-Level AI
>
> FWIW, I implemented ID3 in Forth back in around
> 1984 to support a
> friend's thesis research; it was used to evaluate
> comments made by
> manufacturing engineers on process control.
>
> An issue it raised, which has been mentioned in
> the literature, is
> that it grew rather complex, bushy decision trees.
> I think that might
> be a part of the reason for migration to C4.5
> http://en.wikipedia.org/wiki/C4.5_algorithm
>
>
> On Sun, Jun 1, 2014 at 9:32 AM, Rich Cooper
> <rich@xxxxxxxxxxxxxxxxxxxxxx> wrote:
>> Here is a link to the ID3 algorithm by Ross
> Quinlan:
>>
>> http://en.wikipedia.org/wiki/ID3_algorithm
>>
>>
>>
>> The algorithm iteratively divides the elements
> of S based on all attribute
>> values of all objects, which are said elements
> of S. So it picks the group
>> of features and weights that make the most
> "efficient" split. That
>> separates S into S0 and S1. Its use of the
> "entropy" analogy to evaluate
>> efficiency of the split seems a bit of
> anthropopathy.
>>
>>
>>
>> Has anyone used ID3 to identify classes in
> clustering analysis?
>>
>>
>>
>> -Rich
>>
>>
>>
>> Sincerely,
>>
>> Rich Cooper
>>
>> EnglishLogicKernel.com
>>
>> Rich AT EnglishLogicKernel DOT com
>>
>> 9 4 9 \ 5 2 5 - 5 7 1 2
>>
>>
>>
>>
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