You can subscribe to this list here.
2005 
_{Jan}

_{Feb}

_{Mar}

_{Apr}

_{May}

_{Jun}

_{Jul}

_{Aug}
(1) 
_{Sep}

_{Oct}

_{Nov}
(2) 
_{Dec}


2006 
_{Jan}
(4) 
_{Feb}

_{Mar}

_{Apr}
(3) 
_{May}

_{Jun}
(5) 
_{Jul}
(9) 
_{Aug}

_{Sep}
(3) 
_{Oct}

_{Nov}
(2) 
_{Dec}

2007 
_{Jan}
(6) 
_{Feb}
(5) 
_{Mar}
(3) 
_{Apr}
(7) 
_{May}
(3) 
_{Jun}

_{Jul}

_{Aug}
(7) 
_{Sep}
(8) 
_{Oct}
(6) 
_{Nov}
(1) 
_{Dec}

2008 
_{Jan}

_{Feb}
(3) 
_{Mar}
(9) 
_{Apr}
(2) 
_{May}
(2) 
_{Jun}
(2) 
_{Jul}
(10) 
_{Aug}
(4) 
_{Sep}
(12) 
_{Oct}
(7) 
_{Nov}
(29) 
_{Dec}
(35) 
2009 
_{Jan}
(10) 
_{Feb}
(16) 
_{Mar}
(17) 
_{Apr}
(20) 
_{May}
(42) 
_{Jun}
(19) 
_{Jul}
(32) 
_{Aug}
(8) 
_{Sep}
(2) 
_{Oct}
(2) 
_{Nov}

_{Dec}
(10) 
2010 
_{Jan}
(7) 
_{Feb}
(8) 
_{Mar}
(3) 
_{Apr}

_{May}
(5) 
_{Jun}
(1) 
_{Jul}
(2) 
_{Aug}
(2) 
_{Sep}
(1) 
_{Oct}

_{Nov}
(1) 
_{Dec}

2011 
_{Jan}
(1) 
_{Feb}

_{Mar}

_{Apr}

_{May}

_{Jun}

_{Jul}
(2) 
_{Aug}

_{Sep}
(2) 
_{Oct}

_{Nov}

_{Dec}

2012 
_{Jan}

_{Feb}
(1) 
_{Mar}
(10) 
_{Apr}
(1) 
_{May}
(3) 
_{Jun}

_{Jul}
(7) 
_{Aug}

_{Sep}

_{Oct}

_{Nov}

_{Dec}
(1) 
2013 
_{Jan}

_{Feb}

_{Mar}

_{Apr}

_{May}

_{Jun}

_{Jul}

_{Aug}

_{Sep}

_{Oct}

_{Nov}

_{Dec}
(1) 
2014 
_{Jan}

_{Feb}

_{Mar}

_{Apr}

_{May}
(1) 
_{Jun}

_{Jul}

_{Aug}

_{Sep}

_{Oct}

_{Nov}

_{Dec}

S  M  T  W  T  F  S 

1

2

3
(3) 
4

5

6

7

8

9

10

11

12

13

14

15
(1) 
16
(1) 
17

18

19

20

21

22

23

24

25

26
(1) 
27
(1) 
28

29

30

31





From: Nikita Mishra <nikitamishra07@gm...>  20120727 09:44:00

Hi, I am a student from IIT Kharagpur. I am working on a project related to webquery segmentation and I am using the CRF code provided at http://sourceforge.net/projects/crf/ . I was hoping if a sample file(working example) for semiMarkov CRFAppl(main), is available. It would be really helpful if it could be provided. Thanks and Regards! Nikita Mishra 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@ie...>  20120726 17:59:39

Hello, OK, it seems that in Segment trainer and Nested Trainer, in order to get rid of overflows all metrics are calculated in a log space. Which means that I can't define features that return negative values. I can shift the features but then they wont represent the underlying characteristics correctly. Is there any workaround ? Thanks 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@gm...>  20120716 12:11:53

Hello, I would like to know why during the computation of alpha and beta, you take the log? Is that because of the overflow/underflow problem? If yes, then when you calculate Mi*alpha and Mi*beta you first take the exponent of the matrix/vector elements and then multiply the exponents (and not adding the logs) which brings about the precision errors. Regards 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@ie...>  20120715 16:57:28

Hello, I would like to know why during the computation of alpha and beta, you take the log? Is that because of the overflow/underflow problem? If yes, then when you calculate Mi*alpha and Mi*beta you first take the exponent of the matrix/vector elements and then multiply the exponents (and not adding the logs) which brings about the precision errors. Regards 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@ie...>  20120703 20:03:33

and what is the purpose of variable "base" ? On Tue, Jul 3, 2012 at 1:58 PM, Hamid Reza Hassanzadeh < ha.hassanzadeh@...> wrote: > Thanks a lot. > > > On Tue, Jul 3, 2012 at 1:24 PM, Sunita Sarawagi <sunita@...> wrote: > >> Please follow the definition of Mi_YY.zMult. This function has been >> overridden so that it actually does the right thing for matrix entries >> containing log of the actual values. >> >> >> On 07/03/2012 09:31 PM, Hamid Reza Hassanzadeh wrote: >> >> Hello, >> I have hard times understanding the following lines of codes seen in both >> SegmentTrainer and NestedTrainer which compute the Betas in Log space, I >> would appreciate it if you can help me on that, >> >> initMDone = >> computeLogMi(dataSeq,i,i+ell,featureGenNested,lambda,Mi_YY,Ri_Y,reuseM,initMDone); >> tmp_Y.assign(Ri_Y); >> tmp_Y.assign(beta_Y[i+ell], sumFunc); >> Mi_YY.zMult(tmp_Y, beta_Y[i],1,1,false); >> >> OK, in general to compute beta_Y[i] we should do this, >> computeMi(featureGenerator,lambda,dataSeq,i,Mi_YY,Ri_Y); >> tmp_Y.assign(beta_Y[i]); >> tmp_Y.assign(Ri_Y,multFunc); >> Mi_YY.zMult(tmp_Y, beta_Y[i1]); >> >> Which makes sense to me, but in Log space how can you multiply Mi_YY to >> tmp_Y and add the result to beta_Y? They are in log space. >> >> Regards >> >> >> >> >>  >> Live Security Virtual Conference >> Exclusive live event will cover all the ways today's security and >> threat landscape has changed and how IT managers can respond. Discussions >> will include endpoint security, mobile security and the latest in malware >> threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ >> >> >> >> _______________________________________________ >> Crfusers mailing listCrfusers@...nethttps://lists.sourceforge.net/lists/listinfo/crfusers >> >> > 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@gm...>  20120703 17:58:10

Thanks a lot. On Tue, Jul 3, 2012 at 1:24 PM, Sunita Sarawagi <sunita@...> wrote: > Please follow the definition of Mi_YY.zMult. This function has been > overridden so that it actually does the right thing for matrix entries > containing log of the actual values. > > > On 07/03/2012 09:31 PM, Hamid Reza Hassanzadeh wrote: > > Hello, > I have hard times understanding the following lines of codes seen in both > SegmentTrainer and NestedTrainer which compute the Betas in Log space, I > would appreciate it if you can help me on that, > > initMDone = > computeLogMi(dataSeq,i,i+ell,featureGenNested,lambda,Mi_YY,Ri_Y,reuseM,initMDone); > tmp_Y.assign(Ri_Y); > tmp_Y.assign(beta_Y[i+ell], sumFunc); > Mi_YY.zMult(tmp_Y, beta_Y[i],1,1,false); > > OK, in general to compute beta_Y[i] we should do this, > computeMi(featureGenerator,lambda,dataSeq,i,Mi_YY,Ri_Y); > tmp_Y.assign(beta_Y[i]); > tmp_Y.assign(Ri_Y,multFunc); > Mi_YY.zMult(tmp_Y, beta_Y[i1]); > > Which makes sense to me, but in Log space how can you multiply Mi_YY to > tmp_Y and add the result to beta_Y? They are in log space. > > Regards > > > > >  > Live Security Virtual Conference > Exclusive live event will cover all the ways today's security and > threat landscape has changed and how IT managers can respond. Discussions > will include endpoint security, mobile security and the latest in malware > threats. http://www.accelacomm.com/jaw/sfrnl04242012/114/50122263/ > > > > _______________________________________________ > Crfusers mailing listCrfusers@...nethttps://lists.sourceforge.net/lists/listinfo/crfusers > > 
From: Hamid Reza Hassanzadeh <ha.hassanzadeh@gm...>  20120703 16:01:14

Hello, I have hard times understanding the following lines of codes seen in both SegmentTrainer and NestedTrainer which compute the Betas in Log space, I would appreciate it if you can help me on that, initMDone = computeLogMi(dataSeq,i,i+ell,featureGenNested,lambda,Mi_YY,Ri_Y,reuseM,initMDone); tmp_Y.assign(Ri_Y); tmp_Y.assign(beta_Y[i+ell], sumFunc); Mi_YY.zMult(tmp_Y, beta_Y[i],1,1,false); OK, in general to compute beta_Y[i] we should do this, computeMi(featureGenerator,lambda,dataSeq,i,Mi_YY,Ri_Y); tmp_Y.assign(beta_Y[i]); tmp_Y.assign(Ri_Y,multFunc); Mi_YY.zMult(tmp_Y, beta_Y[i1]); Which makes sense to me, but in Log space how can you multiply Mi_YY to tmp_Y and add the result to beta_Y? They are in log space. Regards 