代码搜索：classification
找到约 3,679 项符合「classification」的源代码
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c ctype.c
/*
* ctype.c: Character classification routines
*
* ====================================================================
* Copyright (c) 2000-2004 CollabNet. All rights reserved.
*
* This soft
www.eeworm.com/read/429426/1948697
py ensemble2.py
# Description: Demonstrates the use of random forests from orngEnsemble module
# Category: classification, ensembles
# Classes: RandomForestLearner
# Uses: bupa.tab
# Referenced: or
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py treelearner.py
# Description: Shows how to construct trees
# Category: learning, decision trees, classification
# Classes: TreeLearner, TreeClassifier, TreeStopCriteria, TreeStopCriteria_common
# Uses:
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py randomclassifier.py
# Description: Shows a classifier that makes random decisions
# Category: classification
# Classes: RandomClassifier
# Uses: lenses
# Referenced: RandomClassifier.htm
import oran
www.eeworm.com/read/415313/11076987
m mchierarchyclassify.m
% MCHierarchyClassify: implementation for hierarchical classification using
% classifier ensembles
%
% Parameters:
% classifier: base classifier
% para: parameters
% 1. PosRatio: ratio of po
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m prex_logdens.m
%PREX_LOGDENS PRTools example on density based classifier improvement
%
% This example shows the use and results of LOGDENS for improving
% the classification in the tail of the distributions
h
www.eeworm.com/read/427254/8957342
m tex.m
% tex.m
%
% This file implements a texture classification example using
% NeuroSolutions for MATLAB.
%
% Problem Definition:
% The problem is to distinguish between the leopard and t
www.eeworm.com/read/176378/9500633
m emtremor.m
% PURPOSE : To demonstrate the EM algoriths for estimating neural network
% weights and signal noise simultaneously. We apply it to a classification
% problem (tremor data - kindly
www.eeworm.com/read/280629/10301847
m emtremor.m
% PURPOSE : To demonstrate the EM algoriths for estimating neural network
% weights and signal noise simultaneously. We apply it to a classification
% problem (tremor data - kindly
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m approximations.m
% approximations: Exact inference for Gaussian process classification is
% intractable, and approximations are necessary. Different approximation
% techniques have been implemented, which all rely on