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Zoltán Szabó  committed cdd4f7d

ITE has been accepted for oral presentation at NIPS-2013: MLOSS workshop, README.md: updated.

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 #ITE (Information Theoretical Estimators)
 
+News:
+
+- ITE has been accepted for oral presentation at [NIPS-2013: MLOSS workshop](http://mloss.org/workshop/nips13/), [PDF](http://www.gatsby.ucl.ac.uk/~szabo/publications/szabo13information.pdf).
+
+* * *
+
 ITE is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities, and kernels on distributions. Thanks to its highly modular design, ITE supports additionally 
 
 - the combinations of the estimation techniques, 
 Note: 
 
 - the evolution of the ITE code is briefly summarized in CHANGELOG.txt.
-- become a [Follower](https://bitbucket.org/szzoli/ite/follow) to be always up-to-date with ITE.
-- if you have an H/I/D/A/C/K estimator/subtask solver with a GPLv3(>=)-compatible license that you would like to be embedded into ITE, feel free to contact me.
+- you can become a [Follower](https://bitbucket.org/szzoli/ite/follow) to be always up-to-date with ITE.
+- if you have a H/I/D/A/C/K estimator/subtask solver \[with a GPLv3(>=)-compatible license, such as GPLv3 or GPLv3(>=)\] that you would like to be embedded into ITE, feel free to contact me.
 
 **Download** the latest release: 
 

File code/estimators/base_estimators/DChiSquare_kNN_k_estimation.m

 %the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
 %
 %This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
-%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
 %
 %You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
 

File code/estimators/base_estimators/DChiSquare_kNN_k_initialization.m

 %the Free Software Foundation, either version 3 of the License, or (at your option) any later version.
 %
 %This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of
-%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public License for more details.
+%MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
 %
 %You should have received a copy of the GNU General Public License along with ITE. If not, see <http://www.gnu.org/licenses/>.
 

File code/estimators/quick_tests/quick_test_CCE.m

                 e1 = e2;            
             %(random) linear transformation applied to the data:
                 A2 = rand(d);
-                A1 = rand * A2; %(e1,A1) => (e2,A2) choice guarantees Y1<<Y2 (in practise, too)                
+                A1 = rand * A2; %(e2,A2) => (e1,A1) choice guarantees Y1<<Y2 (in practise, too)                
             %covariance matrix:
                 cov1 = A1 * A1.';
                 cov2 = A2 * A2.';

File code/estimators/quick_tests/quick_test_DKL.m

                 e1 = e2;
             %(random) linear transformation applied to the data:
                 A2 = rand(d);
-                A1 = rand * A2; %(e1,A1) => (e2,A2) choice guarantees Y1<<Y2 (in practise, too)
+                A1 = rand * A2; %(e2,A2) => (e1,A1) choice guarantees Y1<<Y2 (in practise, too)
             %covariance matrix:
                 cov1 = A1 * A1.';                    
                 cov2 = A2 * A2.';     

File code/estimators/quick_tests/quick_test_DRenyi.m

                 e1 = e2;            
             %(random) linear transformation applied to the data:
                 A2 = rand(d);
-                A1 = rand * A2; %(e1,A1) => (e2,A2) choice guarantees Y1<<Y2 (in practise, too)
+                A1 = rand * A2; %(e2,A2) => (e1,A1) choice guarantees Y1<<Y2 (in practise, too)
             %covariance matrix:
                 cov1 = A1 * A1.';
                 cov2 = A2 * A2.';

File code/estimators/quick_tests/quick_test_KPP.m

                 e1 = e2;
             %(random) linear transformation applied to the data:
                 A2 = rand(d);
-                A1 = rand * A2; %(e1,A1) => (e2,A2) choice guarantees Y1<<Y2 (in practise, too)
+                A1 = rand * A2; %(e2,A2) => (e1,A1) choice guarantees Y1<<Y2 (in practise, too)
             %covariance matrix:
                 cov1 = A1 * A1.';                    
                 cov2 = A2 * A2.';