diff --git a/quapy/CHANGE_LOG.txt b/CHANGE_LOG.txt
similarity index 100%
rename from quapy/CHANGE_LOG.txt
rename to CHANGE_LOG.txt
diff --git a/README.md b/README.md
index 12164a3..d9f697c 100644
--- a/README.md
+++ b/README.md
@@ -13,7 +13,7 @@ for facilitating the analysis and interpretation of the experimental results.
 
 ### Last updates:
 
-* Version 0.1.8 is released! major changes can be consulted [here](quapy/CHANGE_LOG.txt).
+* Version 0.1.8 is released! major changes can be consulted [here](CHANGE_LOG.txt).
 * A detailed documentation is now available [here](https://hlt-isti.github.io/QuaPy/)
 * The developer API documentation is available [here](https://hlt-isti.github.io/QuaPy/build/html/modules.html)
 
diff --git a/quapy/method/aggregative.py b/quapy/method/aggregative.py
index 1fb693c..e07213d 100644
--- a/quapy/method/aggregative.py
+++ b/quapy/method/aggregative.py
@@ -355,8 +355,11 @@ class ACC(AggregativeCrispQuantifier):
         binary) and `B` is the vector of prevalence values estimated via CC, as $x=A^{-1}B$. This solution 
         might not exist for degenerated classifiers, in which case the method defaults to classify and count 
         (i.e., does not attempt any adjustment).
-        Another option is to search for the prevalence vector that minimizes the loss |Ax-B|. The latter is
+        Another option is to search for the prevalence vector that minimizes the L2 norm of |Ax-B|. The latter is
         achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
+        More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
+        Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
+        (LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
     """
 
     def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):
@@ -471,6 +474,18 @@ class PACC(AggregativeSoftQuantifier):
         for `k`). Alternatively, this set can be specified at fit time by indicating the exact set of data
         on which the predictions are to be generated.
     :param n_jobs: number of parallel workers
+    :param solver: indicates the method to be used for obtaining the final estimates. The choice
+        'exact' comes down to solving the system of linear equations `Ax=B` where `A` is a
+        matrix containing the class-conditional probabilities of the predictions (e.g., the tpr and fpr in
+        binary) and `B` is the vector of prevalence values estimated via CC, as $x=A^{-1}B$. This solution
+        might not exist for degenerated classifiers, in which case the method defaults to classify and count
+        (i.e., does not attempt any adjustment).
+        Another option is to search for the prevalence vector that minimizes the L2 norm of |Ax-B|. The latter is
+        achieved by indicating solver='minimize'. This one generally works better, and is the default parameter.
+        More details about this can be consulted in `Bunse, M. "On Multi-Class Extensions of Adjusted Classify and
+        Count", on proceedings of the 2nd International Workshop on Learning to Quantify: Methods and Applications
+        (LQ 2022), ECML/PKDD 2022, Grenoble (France) <https://lq-2022.github.io/proceedings/CompleteVolume.pdf>`_.
+
     """
 
     def __init__(self, classifier: BaseEstimator, val_split=5, n_jobs=None, solver='minimize'):