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-rw-r--r--finale/final_report.tex4
1 files changed, 2 insertions, 2 deletions
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}