On 28 December 2011 14:23, Lars Buitinck wrote:
> 2011/12/28 xinfan meng :
> > In that case, It can also be applied to Chinese word segmentation.
>
> That might actually be a better application than NER. HMMs are very weak
> at NER.
>
Thanks for the input ! I'll have a look at both applications.
2011/12/28 xinfan meng :
> In that case, It can also be applied to Chinese word segmentation.
That might actually be a better application than NER. HMMs are very weak at NER.
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
---
In that case, It can also be applied to Chinese word segmentation.
On Wed, Dec 28, 2011 at 6:49 AM, Lars Buitinck wrote:
> 2011/12/27 Nelle Varoquaux :
> > VDHMMs are HMMs in which a state can emit a serie of observations. It
> seems
> > that the main application to these chains are speech recog
2011/12/27 Nelle Varoquaux :
> VDHMMs are HMMs in which a state can emit a serie of observations. It seems
> that the main application to these chains are speech recognition, and
> handwriting recognition: fields in which we are trying to determine a state
> to poorly segmented data. Would anyone k
On Tue, Dec 27, 2011 at 4:36 PM, Nelle Varoquaux
wrote:
> Hi all,
>
> Despite this not being directly related to scikit-learn, I hope to benefit
> from the experience of machine learning developpers:
> I'm currently studying an article on Variable Duration HMMs (VDHMMs), and I
> am seeking advices
Hi all,
Despite this not being directly related to scikit-learn, I hope to benefit
from the experience of machine learning developpers:
I'm currently studying an article on Variable Duration HMMs (VDHMMs), and I
am seeking advices on application data.
VDHMMs are HMMs in which a state can emit a s
