Re: size of variables in hyperspace?

one big aspect of my project is that it is not an ongoing one!

i haven't explicitly mentioned this, but the whole gradual chain thing
is not really applicable since i'm supposed to only train until the
categorizer is good enough to tag the whole corpus, and then never
train it further.



On Tue, Jul 21, 2009 at 3:24 PM, Ger Hobbelt<[email protected]> wrote:
> Actually, one can think of Chris' concept as a booster of depth=0,
> when you think of the boost chain as limited (like a 'sliding window'
> ;-) ).
> Which makes Chris' the simplest to build, but you can expand it into
> boosting-a-la-Bill by folding & delayed autotraining.
>
> Think of this: assume a booster chain with, say, two stages, i.e. 2
> classifications and thus 2 CSS DBs per category: the initial one (C0),
> the next one which gets trained to correct the errors in C0, hence
> called C1.
> Since C1 must overrule errors in C0, it needs to overpower C0 with its
> verdict, hence that pR = pR(C0) + 2*pR(C1) scheme Bill mentions.
>
> Now the tacky bit is you want to ditch C0 after a while and replace
> it, and the same goes for C1.
> So you've a C2 lurking in the background, which is fed all the vetted
> items, i.e. the entire training feed of C1, and it is trained on those
> as well.
>
> Since C2 is being groomed to replace C0 in a while, it needs more than
> just this, so we also queue a delayed feed of all the other incoming,
> which the verdicts attached. (This system assumes that a verdict by
> C0+C1 is good when a certain delay time T has elapsed; any human
> corrections will have happened before that moment, or they won't make
> it into C2; this is a potential source of lessened quality of C2, but
> even so it'll become better than C0 is, or so we hope. Anyway, after T
> elapses, we can autotrain C2 on the delayed feed as we now 'know' that
> data is correctly vetted by the existing system+human monitors.
>
> After a time X we ditch C0+C1 and put C2 in place as the new C0, while
> starting any subsequent training in a fresh, empty C3, which is the
> new C1.
>
> Further improvement can be had by not just plonking in a fresh empty
> C3 but already prepping it by feeding it any items that C2 didn't
> train well on: sometimes you end up with items that are a bear to
> train, so those can be moved to the C3 training queue while we were
> still using C0+C1 as the live ones, thus producing a completely
> preloaded boost chain C2+C3 when we discard C0+C1.
>
> The benefit of this is that we can still use the boost method to cope
> with the error-only training after this, while during preload we make
> sure the later errors are trained _before_ the ones that don't trouble
> us, thanks to the delayed feed.
>
>
> Just a thought; haven't done this in Real Life yet, but it might be
> worth it for the extra few percent score improvement you might get
> from the boosting approach.
>
>
>
>
>
> On Tue, Jul 21, 2009 at 2:36 PM, Thomas Michael
> Hagen<[email protected]> wrote:
>> chris' solution, where you gradually get rid of the old .chs-files,
>> sounds more implementable than bill's version with doubling weights.
>>
>> i'm now running a new .chs-file for the sports articles, and plan on
>> ditching the old one when the classifier gets confident on the new
>> one.
>>
>> i have not trained enough data to notice ger's comment on big O for
>> hyperspace, but that could become a problem as the amount of data
>> increases. that's partly why i would like to do it chris' way and get
>> rid of old .chs-files every now and then.
>
>
>
>
> --
> Met vriendelijke groeten / Best regards,
>
> Ger Hobbelt
>
> --------------------------------------------------
> web:    http://www.hobbelt.com/
>        http://www.hebbut.net/
> mail:   [email protected]
> mobile: +31-6-11 120 978
> --------------------------------------------------
>

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