diff --git a/DSD/qrs/presentation/images/2d_p1.svg b/DSD/qrs/presentation/images/2d_p1.svg
new file mode 100644
index 0000000..511156e
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p1.svg
@@ -0,0 +1,97 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_p2.svg b/DSD/qrs/presentation/images/2d_p2.svg
new file mode 100644
index 0000000..b1c7994
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p2.svg
@@ -0,0 +1,163 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_p3.svg b/DSD/qrs/presentation/images/2d_p3.svg
new file mode 100644
index 0000000..cf48c33
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p3.svg
@@ -0,0 +1,206 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_p4.svg b/DSD/qrs/presentation/images/2d_p4.svg
new file mode 100644
index 0000000..8d112d8
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p4.svg
@@ -0,0 +1,219 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_p5.svg b/DSD/qrs/presentation/images/2d_p5.svg
new file mode 100644
index 0000000..c86f8fb
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p5.svg
@@ -0,0 +1,182 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_p6.svg b/DSD/qrs/presentation/images/2d_p6.svg
new file mode 100644
index 0000000..58ae427
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_p6.svg
@@ -0,0 +1,182 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/images/2d_view.svg b/DSD/qrs/presentation/images/2d_view.svg
new file mode 100644
index 0000000..35ae60e
--- /dev/null
+++ b/DSD/qrs/presentation/images/2d_view.svg
@@ -0,0 +1,1072 @@
+
+
+
+
diff --git a/DSD/qrs/presentation/presentation.typ b/DSD/qrs/presentation/presentation.typ
index 6195d2c..ada63ea 100644
--- a/DSD/qrs/presentation/presentation.typ
+++ b/DSD/qrs/presentation/presentation.typ
@@ -51,21 +51,33 @@
#align(center)[
#text(weight: "bold")[Metric:] The distance between a sample and a pattern is the minimum normalized distance between the pattern and any pattern-length substring that includes the samples.
#v(1cm)
-#text(weight: "bold")[Decision:] Every sample is receives the label of the closest training pattern.
+#text(weight: "bold")[Decision:] Each sample receives the label of the closest training pattern.
]
]
-
-#slide(title: "Proposed Approach - 2D Interpretation")[
- #figure(
- image("images/overview.svg", height: 100%)
- )
+#slide(title: "2D Interpretation")[
+
+ #only(1)[#figure(image("images/2d_p1.svg", width: 100%))]
+ #only(2)[#figure(image("images/2d_p2.svg", width: 100%))]
+ #only(3)[#figure(image("images/2d_p3.svg", width: 100%))]
+ #only(4)[#figure(image("images/2d_p4.svg", width: 100%))]
+ #only(5)[#figure(image("images/2d_p5.svg", width: 100%))]
]
-#slide(title: "Proposed Approach - 2D Interpretation")[
+#slide(title: "Question")[
+#align(center)[Should the algorithm #text(weight: "bold")[always] choose a label?]
+]
+
+#slide(title: "2D Interpretation")[
+
+ #figure(image("images/2d_p6.svg", width: 100%))
+]
+
+#slide(title: "Parameter "+sym.alpha)[
#figure(
image("images/areas.svg", width: 100%)
)
+#align(center)[With $alpha lt.triple 2$, the algorithm acquire novelty-detection capability.]
]
#slide(title: "Parameter")[]