代码搜索:Classify

找到约 2,639 项符合「Classify」的源代码

代码结果 2,639
www.eeworm.com/read/129915/14217605

m classification_error.m

function [classify, err] = classification_error(D, features, targets, region) %Find a classification error for a given decision surface D and a given set of %features (2xL) and targets (1xL) %The
www.eeworm.com/read/474600/6813413

m classification_error.m

function [classify, err] = classification_error(D, patterns, targets, region) %Find a classification error for a given decision surface D and a given set of %patterns (2xL) and targets (1xL) %The
www.eeworm.com/read/415311/11077027

m classification_error.m

function [classify, err] = classification_error(D, features, targets, region) %Find a classification error for a given decision surface D and a given set of %features (2xL) and targets (1xL) %The
www.eeworm.com/read/196264/8109105

makefile

# # makefile for svm_light # # Thorsten Joachims, 2002 # # Thanks to Ralf Herbrich for the initial version. # all: @echo "" @echo "USAGE make [win|linux]" @echo "" linux: make -f
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rb 06 - writing block methods that classify or collect.rb

module Enumerable def find_no_more_than(limit) inject([]) do |a,e| a = limit a end end end #--- a = [1, 2, 3, 4, 5, 6, 7, 8, 9, 1
www.eeworm.com/read/278885/10492263

java,v selectionarea.java,v

head 1.4; access; symbols; locks; strict; comment @# @; 1.4 date 2005.06.10.18.54.05; author rirwin; state Exp; branches; next 1.3; 1.3 date 2005.05.24.16.14.39; author rirwin; state Exp; branches;
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rb 15 - partitioning or classifying a set.rb

require 'set' s = Set.new((1..10).collect) # => # # Divide the set into the "true" subset and the "false" subset: that # is, the "less than 5" subset and the "no
www.eeworm.com/read/197608/7069726

c oddeven.c

/* * File: oddeven.c * --------------- * Reads in a number and classifies it as even or odd. */ #include #include "genlib.h" #include "simpio.h" main() { int n; printf("Progra
www.eeworm.com/read/436088/7777289

cs iclassifier.cs

namespace FinanceAI.AI { public interface IClassifier { // Classify the given sample return the category and add it to the sample string Classify( ISample sample );