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authorThibaut Horel <thibaut.horel@gmail.com>2015-05-18 23:32:07 +0200
committerThibaut Horel <thibaut.horel@gmail.com>2015-05-18 23:32:07 +0200
commit39a72f9f8127970470f46529ff8f1cc1a22450d9 (patch)
treef98ce40bf838eaecb15bd4521d2065acd4ab5c6a
parent880e6a03d99d2ee4af6839eb2c13f9664765b1b3 (diff)
downloadcascades-39a72f9f8127970470f46529ff8f1cc1a22450d9.tar.gz
More changes in the appendix
-rw-r--r--paper/sections/appendix.tex8
1 files changed, 4 insertions, 4 deletions
diff --git a/paper/sections/appendix.tex b/paper/sections/appendix.tex
index 5a6a2c7..32e2775 100644
--- a/paper/sections/appendix.tex
+++ b/paper/sections/appendix.tex
@@ -134,7 +134,7 @@ Figure~\ref{fig:running_time_n_nodes}.}
We include here a running time analysis of our algorithm. In
Figure~\ref{fig:running_time_n_nodes}, we compared our algorithm to the
-benchmarks for increasing values of the number of nodes. In
+benchmark algorithms for increasing values of the number of nodes. In
Figure~\ref{fig:running_time_n_cascades}, we compared our algorithm to the
benchmarks for a fixed graph but for increasing number of observed cascades.
@@ -143,9 +143,9 @@ Even though both the MLE algorithm and the algorithm we introduced are based on
convex optimization, the MLE algorithm is faster. This is due to the overhead
caused by the $\ell_1$-regularisation in~\eqref{eq:pre-mle}.
-The dependency as the number of cascades increases is linear, as expected. The
-slope is largest for our algorithm, which is against caused by the overhead
-induced by the $\ell_1$-regularization.
+The dependency of the running time on the number of cascades increases is
+linear, as expected. The slope is largest for our algorithm, which is against
+caused by the overhead induced by the $\ell_1$-regularization.