> > In this problem there was little or no spatial hierarchy, the solution > mostly required discovering the temporal statistics, i.e. predicting where > the user would click next by recognizing typical user patterns. The > temporal learning algorithms in HTMs are ideal for such problems."
As an idea for differentiating input spatially, what about some kind of demographic encoder where some kind of location encoding is provided according to a position in some kind of semantic spectrum? Cheers, David On Mon, Feb 1, 2016 at 1:07 PM, Pascal Weinberger <[email protected] > wrote: > Working on meetgaia.com and nostradamiq.org :) > > Iraq you're interested on details, ping me personally :) > > > > Best, > > Pascal Weinberger > > ____________________________ > > BE THE CHANGE YOU WANT TO SEE IN THE WORLD ... > > > PLEASE NOTE: This email and any file transmitted are strictly > confidential and/or legally privileged and intended only for the person(s) > directly addressed. If you are not the intended recipient, any use, > copying, transmission, distribution, or other forms of dissemination is > strictly prohibited. If you have received this email in error, please > notify the sender immediately and permanently delete the email and files, > if any. > Please consider the environment before printing this message. > > > > > On 01 Feb 2016, at 16:46, dokondr <[email protected]> wrote: > > > > Paper titled: > > "HIERARCHICAL TEMPORAL MEMORY > > including > > HTM Cortical Learning Algorithms > > VERSION 0.2.1, SEPTEMBER 12, 2011 > > ©Numenta, Inc. 2011 > > > > among other things states: > > > > "But many valuable problems are simpler than vision, and a single HTM > region might prove sufficient. For example, we applied an HTM to predicting > where a person browsing a website is likely to click next. This problem > involved feeding the HTM network streams of web click data. In this problem > there was little or no spatial hierarchy, the solution mostly required > discovering the temporal statistics, i.e. predicting where the user would > click next by recognizing typical user patterns. The temporal learning > algorithms in HTMs are ideal for such problems." > > > > Has anyone tried to implement such an application? > > Please provide links to research, papers on similar, web-related > projects. > > > > Many thanks, > > Dmitri > > -- *With kind regards,* David Ray Java Solutions Architect *Cortical.io <http://cortical.io/>* Sponsor of: HTM.java <https://github.com/numenta/htm.java> [email protected] http://cortical.io
