代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/307651/13718051
m knn_light.m
% knn_light: K-Nearest Neighbor classification using euclid distance
%
% [C] = knn_light(data, proto, protoClass, [K])
%
% Input and output arguments ([]'s are optional):
% data (matrix) of
www.eeworm.com/read/307388/13723484
asv knn_light.asv
% knn_light: K-Nearest Neighbor classification using euclid distance
%
% [C] = knn_light(data, proto, protoClass, [K])
%
% Input and output arguments ([]'s are optional):
% data (matrix) of
www.eeworm.com/read/307388/13723486
m knn_light.m
% knn_light: K-Nearest Neighbor classification using euclid distance
%
% [C] = knn_light(data, proto, protoClass, [K])
%
% Input and output arguments ([]'s are optional):
% data (matrix) of
www.eeworm.com/read/128684/5980329
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each column represent
www.eeworm.com/read/124910/6035857
s s_fpclassify.s
/* Return classification value corresponding to argument.
Copyright (C) 2000, 2002 Free Software Foundation, Inc.
This file is part of the GNU C Library.
The GNU C Library is free software;
www.eeworm.com/read/121089/6070674
h ctype.h
/***
*ctype.h - character conversion macros and ctype macros
*
* Copyright (c) 1985-1990, Microsoft Corporation. All rights reserved.
*
*Purpose:
* Defines macros for character classification/c
www.eeworm.com/read/402363/6343571
m demsvm2.m
function demsvm2()
% DEMSVM2 - Demonstrate advanced Support Vector Machine features
%
% DEMSVM2 demonstrates the classification of a simple artificial data
% set by a Support Vector Machine class
www.eeworm.com/read/493294/6399897
m gendats.m
%GENDATS Generation of a simple classification problem of 2 Gaussian classes
%
% A = GENDATS (N,K,D,LABTYPE)
%
% INPUT
% N Dataset size, or 2-element array of class sizes (default: [50 50]
www.eeworm.com/read/493294/6400289
m featself.m
%FEATSELF Forward feature selection for classification
%
% [W,R] = FEATSELF(A,CRIT,K,T,FID)
% [W,R] = FEATSELF(A,CRIT,K,N,FID)
%
% INPUT
% A Training dataset
% CRIT Name of the criterion or u
www.eeworm.com/read/493294/6400485
m featsellr.m
%FEATSELLR Plus-L-takeaway-R feature selection for classification
%
% [W,RES] = FEATSELLR(A,CRIT,K,L,R,T,FID)
%
% INPUT
% A Dataset
% CRIT String name of the criterion or untrained mapping