代码搜索：Nearest
找到约 1,596 项符合「Nearest」的源代码
代码结果 1,596
www.eeworm.com/read/170936/9779259
m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with prio
www.eeworm.com/read/415313/11076514
m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with prio
www.eeworm.com/read/415311/11077016
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
www.eeworm.com/read/413912/11137222
m demknn1.m
%DEMKNN1 Demonstrate nearest neighbour classifier.
%
% Description
% The problem consists of data in a two-dimensional space. The data is
% drawn from three spherical Gaussian distributions with prio
www.eeworm.com/read/410924/11264764
m nearestneighborediting.m
function D = NearestNeighborEditing(train_features, train_targets, params, region)
% Classify points using the nearest neighbor editing algorithm
% Inputs:
% train_features - Train features
% t
www.eeworm.com/read/470729/6906776
in cssproperties.in
#
# all valid CSS2 properties.
#
# aural properties are commented out, as we don't support them anyway.
#
# some properties are used in khtml, but are not part of CSS. They are used to get
# HTM
www.eeworm.com/read/340194/12174250
in cssproperties.in
#
# all valid CSS2 properties.
#
# aural properties are commented out, as we don't support them anyway.
#
# some properties are used in khtml, but are not part of CSS. They are used to get
# HTM
www.eeworm.com/read/191902/8417328
m store_grabbag.m
function D = Store_Grabbag(train_features, train_targets, Knn, region)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% features - Train features
www.eeworm.com/read/431675/8661709
m modeseek.m
%MODESEEK Clustering by modeseeking
%
% [labels,J] = modeseek(D,k)
%
% If D is a n*n distance matrix between object then a k-nn
% modeseeking method is used to assign each object to its nearest
%
www.eeworm.com/read/177129/9468945
m store_grabbag.m
function D = Store_Grabbag(train_features, train_targets, Knn, region)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% features - Train features