doc update

chapter06/knKmeans.m

@@ -4,9 +4,9 @@
 %   K: n x n kernel matrix
 %   init: either number of clusters (k) or initial label (1xn)
 % Output:
-%   label: 1 x n clustering result label
-%   energy: optimization target value
+%   label: 1 x n sample labels
 %   model: trained model structure
+%   energy: optimization target value
 % Reference: Kernel Methods for Pattern Analysis
 % by John Shawe-Taylor, Nello Cristianini
 % Written by Mo Chen ([email protected]).

chapter09/kmeans.m

@@ -4,7 +4,7 @@
 %   X: d x n data matrix
 %   init: k number of clusters or label (1 x n vector)
 % Output:
-%   label: 1 x n cluster label
+%   label: 1 x n sample labels
 %   mu: d x k center of clusters
 %   energy: optimization target value
 % Written by Mo Chen ([email protected]).

chapter09/kmedoids.m

@@ -4,7 +4,7 @@
 %   X: d x n data matrix
 %   init: k number of clusters or label (1 x n vector)
 % Output:
-%   label: 1 x n cluster label
+%   label: 1 x n sample labels
 %   index: index of medoids
 %   energy: optimization target value
 % Written by Mo Chen ([email protected]).

chapter09/litekmeans.m

@@ -11,6 +11,6 @@
 %   X: d x n data matrix
 %   label: initial sample labels
 % Output:
-%   label: 1 x n sample label
+%   label: 1 x n sample labels
 %   mu: d x k center of clusters
 % Written by Mo Chen ([email protected]).