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/13911/286885
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/216959/4877715
rd classagreement.rd
\name{classAgreement}
\alias{classAgreement}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Coefficients comparing classification agreement}
\description{
\code{classAgreement
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/429426/1948852
py cb-learner.py
# Description: Shows how to derive a Python class form orange.Learner
# Category: classification, learning, callbacks to Python
# Classes: Learner, ContingencyAttrClass, orngMisc.BestOnTheFly
www.eeworm.com/read/411379/2188961
m getnsv.m
function nsv = getnsv(net)
% GETNSV
%
% Accessor method returning the number of support vectors of a support vector
% classification network.
%
% n = getnsv(net);
%
% File : @svc/
www.eeworm.com/read/411379/2188963
m strip.m
function net = strip(net, tolerance)
% STRIP
%
% Delete support vectors from a support vector classification network for which
% the magnitude of the corresponding weight is less than a given to
www.eeworm.com/read/359369/2978530
m demtrain.m
function demtrain(action);
%DEMTRAIN Demonstrate training of MLP network.
%
% Description
% DEMTRAIN brings up a simple GUI to show the training of an MLP
% network on classification and re
www.eeworm.com/read/352665/3093806
rd classagreement.rd
\name{classAgreement}
\alias{classAgreement}
%- Also NEED an `\alias' for EACH other topic documented here.
\title{Coefficients comparing classification agreement}
\description{
\code{classAgreement
www.eeworm.com/read/393436/8287403
m impsampdemo.m
%This is a simple demonstration of the approximate method for GP based
%classification over multiple classes which is presented in
%
% Girolami, M., Rogers, S.,
% Variational Bayesian Multinomial
www.eeworm.com/read/415313/11076728
m wekaclassify.m
% WekaClassify: implementation for weka classification
%
% Parameters:
% para: parameters
% 1. MultiClassWrapper: use multi-class wrapper or not, default: -1
% (automatically detected)
% X_