From 3fe7048cfee40603a5727f16f609e9256ed68ff1 Mon Sep 17 00:00:00 2001
From: Thibaut Horel <thibaut.horel@gmail.com>
Date: Fri, 11 Dec 2015 18:09:05 -0500
Subject: Minor reformulation in abstract

---
 finale/final_report.tex | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'finale/final_report.tex')

diff --git a/finale/final_report.tex b/finale/final_report.tex
index 4203fa5..522e3a7 100644
--- a/finale/final_report.tex
+++ b/finale/final_report.tex
@@ -76,8 +76,8 @@ Diffusion Processes}
     Maximum-Likelihood estimator for the edge weights, a Bayesian treatment of
     the problem is still lacking. In this work, we establish a scalable Bayesian
     framework for the unified NIP formulation of \cite{pouget}. Furthermore, we
-    show how this Bayesian framework leads to intuitive and effective heuristics
-    to greatly speed up learning.
+    show how this Bayesian framework leads to intuitive and effective active
+    learning heuristics which greatly speed up learning.
 \end{abstract}
 
 \section{Introduction}
-- 
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