代码搜索：Boosting
找到约 146 项符合「Boosting」的源代码
代码结果 146
www.eeworm.com/read/482389/6624063
c boost-main.c
/******************************************************************************
boost-main.c - main driver program for experiments with boosting
largely pilfered from ripper-main.c
****************
www.eeworm.com/read/429426/1948678
py ensemble.py
# Description: Demonstrates the use of boosting and bagging from orngEnsemble module
# Category: classification, ensembles
# Classes: BoostedLearner, BaggedLearner
# Uses: lymphograph
www.eeworm.com/read/480116/6677149
index
ada Fitting Stochastic Boosting Models
addtest Add a test set to ada
pairs.ada Pairwise Plots and Variable Importancs Plot for
www.eeworm.com/read/429426/1948744
py domain13.py
# Description: Adds two new numerical attributes to iris data set, and tests through cross validation if this helps in boosting classification accuracy
# Category: modelling
# Uses: iris
www.eeworm.com/read/367675/2837917
txt 253.txt
发信人: WbAI (wbAI), 信区: DataMining
标 题: 谁能谈一下用<mark>boosting</mark>方法作文本分类的可行性？
发信站: 南京大学小百合站 (Sun Oct 13 20:19:14 2002)
<mark>boosting</mark>方法从原理上讲可以用于任何分类器，但我想，对于文本分类，由于特征太多
等原因，该方法似乎效率会特别低下，甚至是不可行的哟。谁能谈谈<mark>boosting</mark>方法对文 ...
www.eeworm.com/read/367675/2838050
txt 338.txt
发信人: jeff814 (mimi), 信区: DataMining
标 题: 请教：<mark>boosting</mark>方法的实验结果为何会是**？？
发信站: 南京大学小百合站 (Thu Oct 17 09:20:10 2002)
我现在用<mark>boosting</mark>+决策树的方法做分类，希望性能比单纯用决策树好。但实际是随着迭代
轮数的增加，得到的假设hi的权重反而在减小。即：<mark>boosting</mark>+决策树得 ...
www.eeworm.com/read/397106/8067722
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, Iterations, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
%
www.eeworm.com/read/429426/1948716
py ensemble3.py
# Description: Bagging and boosting with k-nearest neighbors
# Category: modelling
# Uses: promoters.tab
# Classes: orngTest.crossValidation, orngEnsemble.BaggedLearner, orngEnsemble.
www.eeworm.com/read/191902/8417279
m locboost.m
function [D, P, theta, phi] = LocBoost(features, targets, params, region)
% Classify using the local boosting algorithm
% Inputs:
% features - Train features
% targets - Train targets
% par