Your explanations are invaluable.  I look forward to reviewing the next
release for the improvements and can't wait to see how our corpus improves
now that HMM is being used.

Thank you again.  I could go on discussing forever - thanks for stopping me.



On Mon, May 4, 2015 at 1:39 PM, Thomas Eckardt <thomas.ecka...@thockar.com>
wrote:

> >Why wouldn't we
> >want the score be reduced for low confidence?
>
> because the PenaltyBox is working this way - every feature sets, counts,
> calculates or weights results to resulting scores
>
> Calculating/weighting scores base on the Bayes/HMM results is still
> implemented. The next build will use a better code for this.
>
> ....
> The Bayesian check will only run, if the spam/ham probability of the HMM
> check is in a given value range or the HMM check has given too few results
> or
> the confidence ( baysConf ) of the detection is too low.
> ....
>
>
> How ever you need to configure 'baysConf'! assp will get a probability in
> every case, but this says nothing about the confidence.
>
> Here are two extreme examples:
>
> you can get a probability of 0.650 with a confidence of 0.5 - which is
> 100% spam - no doubt
> and
> you can get a probability of 0.999 with a confidence of 0.00001 - which is
> a very wild guess (50.00001%(spam) to 49.99999%(ham))
>
> 'baysConfidenceHalfScore' will reduce the score to at least the half for
> both checks - so the result will be max. the full.
>
> >is correct math, but where do the 12 and 3 numbers come from?
>
> I don't want to discuss the Bayesian/HMM math. These are simple numbers,
> everyone will understand them - less results -> less confidence.
> ASSP would not need these numbers, if everyone has a correct corpus
> (corpusnorm). These numbers are something like milestones, if they are
> reached, the code loses trust to the math of the confidence and switches
> the general behavior (block -> score -> monitor).
>
> I stop this discussion here. Both features are working nearly perfect for
> years now - in my case with less than 0.5% correction rate for each and
> less than 0.25% together, if the corpus is correct.
>
> Thomas
>
>
>
>
>
> Von:    K Post <nntp.p...@gmail.com>
> An:     ASSP development mailing list <assp-test@lists.sourceforge.net>
> Datum:  04.05.2015 18:28
> Betreff:        Re: [Assp-test] HMM and Bayesian Running
>
>
>
> This is now starting to make more sense to me.  Thank you for the
> explanations.
>
> 6 = 60 / 12 +1
> 21 = 60 / 3 +1
> is correct math, but where do the 12 and 3 numbers come from?
>
> My BayConf is 0, and intentionally so because my norm is 1.0000 or very
> close to that.  That's correct logic right?
> And yes, bayesProbability is 0.60
>
> I guess my original question still stands: Why not only count EITHER hmm
> or
> bayes if confidence is low on both?  And why would "the confidence is very
> low - but it is ignored for the resulting score" be true.  Why wouldn't we
> want the score be reduced for low confidence?
>
> Under BayesAfterHMM is says:
> The Bayesian check will only run, if the spam/ham probability of the HMM
> check is in a given value range or the HMM check has given too few
> results.
>
> Is there an equivalent, easy to implement, and *sensible* solution that is
> something like:
> HMMScoreDiscardIfOutsideOfRange:
> The HMM score will only be counted if the HMM value range is outside of
> this range.  Setting this range to something like 0.35-0.65, when paired
> with BayesAfterHMM (which will allow Bayesian to run after an HMM check)
> stops the HMM score from being added to the bayesian score which would
> result in an unreasonably high score.
>
> I'm kind of just thinking out loud here.  Please tell me if what I'm
> suggesting doesn't make sense or if I need to be more clear.
>
> On Mon, May 4, 2015 at 3:50 AM, Thomas Eckardt
> <thomas.ecka...@thockar.com>
> wrote:
>
> > >In your example, how is the 6 or 21 number calculated based on a
> > >maxBayesValues of 60?
> >
> > 6 = 60 / 12 +1
> > 21 = 60 / 3 +1
> >
> > >I've got BayesAfterHMM set to 0.3-0.7
> >
> > I assume 'baysProbability' is set to 0.6 - and 'baysConf' is set to zero
> -
> > right ?
> >
> > If assp got at least 6 but less than 21 results and the resulting HMM
> > probability  is for example 0.65  :
> >
> > - DoHMM is set to score for this mail
> > - Bayes will run, because 0.65 is less than 0.7 and more than 0.3
> > - the confidence is very low - but it is ignored for the resulting score
> > - without 'baysConf',  0.65 is 100% SPAM because  'baysProbability' is
> set
> > to 0.6
> >
> > The mail gets the full HMM score + the full Bayesian score.
> >
> >
> > Thomas
> >
> >
> >
> >
> > Von:    K Post <nntp.p...@gmail.com>
> > An:     ASSP development mailing list <assp-test@lists.sourceforge.net>
> > Datum:  03.05.2015 22:15
> > Betreff:        Re: [Assp-test] HMM and Bayesian Running
> >
> >
> >
> > In your example, how is the 6 or 21 number calculated based on a
> > maxBayesValues of 60?
> >
> > And more importantly, with my example, where there was 49 scored because
> > of
> > HMM, and then another 49 scored for bayesian, also which wasn't
> > particularly certain.  Net result: a really high score - not really
> > accurate.  Is there some why to tell ASSP that if HMM and Bayes both
> > assign
> > scores, and HMM is based on a really low number of results, just ignore
> > the
> > HMM result?  I feel like that would be more accurate.
> >
> > On Sun, May 3, 2015 at 2:55 AM, Thomas Eckardt
> > <thomas.ecka...@thockar.com>
> > wrote:
> >
> > > It works this way.
> > >
> > > For example, if 'maxBayesValues' is set to 60
> > >
> > > - DoHMM is set to monitor if less than 6 results are found -> Bayes
> will
> > > run
> > > - DoHMM is set to score, if DoHMM is configured to block and less than
> > 21
> > > results are found - Bayes will run on 'BayesAfterHMM'
> > >
> > >  Thomas
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > >
> > > Von:    K Post <nntp.p...@gmail.com>
> > > An:     ASSP development mailing list
> <assp-test@lists.sourceforge.net>
> > > Datum:  02.05.2015 16:34
> > > Betreff:        [Assp-test] HMM and Bayesian Running
> > >
> > >
> > >
> > > I've got BayesAfterHMM set to 0.3-0.7
> > >
> > > Sometimes, I see bayesian run even though HMM scores a probability of
> > > 1.00000.  Is that right?  Could this be happening because HMM doesn't
> > have
> > > enough hits?  If that's the case can we tell ASSP to ignore HMM it's
> > > unsure
> > > and use Bayesian INSTEAD (only) in these cases?
> > >
> > > Sorry for the HMM questions, but the concept's new to me.
> > >
> > >
> > > Clearly my databases aren't quite perfect, but I was hoping that
> > > senderbase
> > > would bring the HMM score down enough to pass it.
> > >
> > > However, with Bayesian running too, we exceed the max and it's
> blocked.
> > >
> > > X-ASSP-Message-Score: -35 (White Organization/Domain
> > > 'RESPONSYS')
> > > X-ASSP-IP-Score: -34 (White Organization/Domain 'RESPONSYS')
> > > X-ASSP-Detected-URI: rsys2.com(1), e.hotwire.com(1),
> > > hotwire.com(7)
> > > X-ASSP-Message-Score: 49 (HMM Probability: 1.0000)
> > > X-ASSP-IP-Score: 49 (HMM Probability: 1.0000)
> > > X-ASSP-Message-Score: 49 (Bayesian Probability: 1.00000)
> > > X-ASSP-IP-Score: 49 (Bayesian Probability: 1.00000)
> > > X-ASSP-Spam-Prob: 1.00000
> > > X-ASSP-HMM-Spam-Prob: 1.00000
> > > X-ASSP-Tag: MessageLimit
> > > X-ASSP-Spam-Reason: MessageScore 63, limit 50
> > > X-ASSP-Message-Totalscore: 63
> > >
> > >
> >
> >
>
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