UdeM-McGill-mPrime machine learning seminar Mon. March 12th @ 15h00, Université de Montréal, room TBA (tentatively, AA3195).
Pardis Noorzad will be giving an mPrime talk tomorrow (Monday, March 12th) at 15h00. The talk will tentatively be held in AA3195, of the Pavillon Andre-Aisenstadt http://www.umontreal.ca/plancampus/pavillons/pav20.html at the Université de Montréal. An email will be sent tomorrow morning should there be a change in location. Hope to see you there !
When: Tomorrow, Monday March 12th, 2012 Where: TBA, tentatively AA3195
Title: "SPARROW: Sparse approximation weighted regression"
Abstract: We propose sparse approximation weighted regression (SPARROW), which is a nonparametric method of regression that takes advantage of the sparse linear approximation of a query point. SPARROW employs weights based on sparse approximation in the context of locally constant, locally linear, and locally quadratic regression to generate better estimates than for e.g., k-nearest neighbor regression and more generally, kernel-weighted local polynomial regression. Our experimental results show that SPARROW performs competitively.
This is joint work with Prof. Bob L. Sturm.
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A reminder for today's mPrime talk by Pardis Noorzad. The room has been confirmed: AA3195 as usual !
On Sun, Mar 11, 2012 at 9:24 PM, Guillaume Desjardins < guillaume.desjardins@gmail.com> wrote:
Pardis Noorzad will be giving an mPrime talk tomorrow (Monday, March 12th) at 15h00. The talk will tentatively be held in AA3195, of the Pavillon Andre-Aisenstadt http://www.umontreal.ca/plancampus/pavillons/pav20.html at the Université de Montréal. An email will be sent tomorrow morning should there be a change in location. Hope to see you there !
When: Tomorrow, Monday March 12th, 2012 Where: AA3195
Title: "SPARROW: Sparse approximation weighted regression"
Abstract: We propose sparse approximation weighted regression (SPARROW), which is a nonparametric method of regression that takes advantage of the sparse linear approximation of a query point. SPARROW employs weights based on sparse approximation in the context of locally constant, locally linear, and locally quadratic regression to generate better estimates than for e.g., k-nearest neighbor regression and more generally, kernel-weighted local polynomial regression. Our experimental results show that SPARROW performs competitively.
This is joint work with Prof. Bob L. Sturm.
lisa_seminaires@iro.umontreal.ca
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Guillaume Desjardins