代码搜索：classification
找到约 3,679 项符合「classification」的源代码
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www.eeworm.com/read/191902/8417137
m contents.m
% Classification GUI and toolbox
% Version 1.0
%
% GUI start commands
%
% classifier - Start the classification GUI
% enter_distributions - Starts the parameter input screen (used by classif
www.eeworm.com/read/389442/8519786
m demop1.m
%% Classification with a 2-input Perceptron
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.
www.eeworm.com/read/289488/8548284
m contents.m
% contents.m
%
% This directory contains a example files that illustrates
% some of the algorithm developed in the toolsvm or toolreg
%
% File | Comments
%
www.eeworm.com/read/289324/8559241
m normal.m
% [ll, f, cl, P] = normal(data, alpha, mu, sigma)
%
% This function serves several purposes:
% - evaluate PDF of normal mixture at each of the data points
% - evaluate log-likelihood of the data
www.eeworm.com/read/388092/8636300
m demop1.m
%% Classification with a 2-input Perceptron
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.
www.eeworm.com/read/287267/8699012
m contents.m
% contents.m
%
% This directory contains a example files that illustrates
% some of the algorithm developed in the toolsvm or toolreg
%
% File | Comments
%
www.eeworm.com/read/287267/8699042
m contents.m
% contents.m
%
% This directory contains a example files that illustrates
% some of the algorithm developed in the toolsvm or toolreg
%
% File | Comments
%
www.eeworm.com/read/286662/8751751
m contents.m
% Classification GUI and toolbox
% Version 1.0
%
% GUI start commands
%
% classifier - Start the classification GUI
% enter_distributions - Starts the parameter input screen (used by classif
www.eeworm.com/read/376531/9315240
rd randomforest.rd
\name{randomForest}
\alias{randomForest}
\alias{randomForest.formula}
\alias{randomForest.default}
\alias{print.randomForest}
\title{Classification and Regression with Random Forest}
\description{
www.eeworm.com/read/376531/9315252
news
Wishlist (formerly TODO):
* Implement the new scheme of handling classwt in classification.
* Allow categorical predictors with more than 32 categories.
* Use more compact storage of proximity matr