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  • 語言: 其他
  • 標簽: Matlab??

資源簡介

多輸出支持向量回歸 對于一般的回歸問題,給定訓練樣本D={(x1,y1),(x2,y2),...,(xn,yn)},yi€R,我們希望學習到一個f(x)使得其與y盡可能的接近,w,b是待確定的參數。在這個模型中,只有當f(x)與y完全相同時,損失才為零,而支持向量回歸假設我們能容忍的f(x)與y之間最多有ε的偏差,當且僅當f(x)與y的差別絕對值大于ε時,才計算損失,此時相當于以f(x)為中心,構建一個寬度為2ε的間隔帶,若訓練樣本落入此間隔帶,則認為是被預測正確的。(間隔帶兩側的松弛程度可有所不同) ------

資源截圖

代碼片段和文件信息

function?RESULTS?=?assessment(LabelsPreLabelspar)

%
%???function?RESULTS?=?assessment(LabelsPreLabelspar)
%
%???INPUTS:
%
%???Labels?????????:?A?vector?containing?the?true?(actual)?labels?for?a?given?set?of?sample.
%???PreLabels??????:?A?vector?containing?the?estimated?(predicted)?labels?for?a?given?set?of?sample.
% par ???:?‘class‘?or?‘regress‘
%
%???OUTPUTS:?(all?contained?in?struct?RESULTS)
%
%???ConfusionMatrix:?Confusion?matrix?of?the?classification?process?(True?labels?in?columns?predictions?in?rows)
%???Kappa??????????:?Estimated?Cohen‘s?Kappa?coefficient
%???OA?????????????:?Overall?Accuracy
%???varKappa???????:?Variance?of?the?estimated?Kappa?coefficient
%???Z??????????????:?A?basic?Z-score?for?significance?testing?(considering?that?Kappa?is?normally?distributed)
%???CI?????????????:?Confidence?interval?at?95%?for?the?estimated?Kappa?coefficient
%???Wilcoxon?sign?test?and?McNemar‘s?test?of?significance?differences
%
%???Gustavo?Camps-Valls?2007(c)
%???gcamps@uv.es
%???
%???Formulae?in:
%???Assessing?the?Accuracy?of?Remotely?Sensed?Data
%???by?Russell?G?Congalton?Kass?Green.?CRC?Press
%

switch?lower(par)
case?{‘class‘}

Etiquetas?=?union(LabelsPreLabels);?????%?Class?labels?(usually?123....?but?can?work?with?text?labels)
NumClases?=?length(Etiquetas);?%?Number?of?classes

%?Compute?confusion?matrix
????ConfusionMatrix?=?zeros(NumClases);
for?i=1:NumClases
????????for?j=1:NumClases
????????????????ConfusionMatrix(ij)?=?length(find(PreLabels==Etiquetas(i)?&?Labels==Etiquetas(j)));
????????end;
end;
??????????
%?Compute?Overall?Accuracy?and?Cohen‘s?kappa?statistic
n??????=?sum(ConfusionMatrix(:));?????????????????????%?Total?number?of?samples
PA?????=?sum(diag(ConfusionMatrix));
OA?????=?PA/n;

%?Estimated?Overall?Cohen‘s?Kappa?(suboptimal?implementation)
npj?=?sum(ConfusionMatrix1);
nip?=?sum(ConfusionMatrix2);
PE??=?npj*nip;
????if?(n*PA-PE)?==?0?&&?(n^2-PE)?==?0
????????%?Solve?indetermination
????????warning(‘0?divided?by?0‘)
????????Kappa?=?1;
????else
???? Kappa??=?(n*PA-PE)/(n^2-PE);
????end

%?Cohen‘s?Kappa?Variance
theta1?=?OA;
theta2?=?PE/n^2;
theta3?=?(nip‘+npj)?*?diag(ConfusionMatrix)??/?n^2;

suma4?=?0;
for?i=1:NumClases
for?j=1:NumClases
suma4?=?suma4?+?ConfusionMatrix(ij)*(nip(i)?+?npj(j))^2;
end;
end;
theta4?=?suma4/n^3;
varKappa?=?(?theta1*(1-theta1)/(1-theta2)^2?????+?????2*(1-theta1)*(2*theta1*theta2-theta3)/(1-theta2)^3??????+?????(1-theta1)^2*(theta4-4*theta2^2)/(1-theta2)^4??)/n;
Z?=?Kappa/sqrt(varKappa);
CI?=?[Kappa?+?1.96*sqrt(varKappa)?Kappa?-?1.96*sqrt(varKappa)];

if?NumClases==2
????%?Wilcoxon?test?at?95%?confidence?interval
????[p1h1]?=?signrank(LabelsPreLabels);
????if?h1==0
????????RESULTS.WilcoxonComment =?‘The?null?hypothesis?of?both?distributions?come?from?the?same?median?can?be?rejected?at?the?5%?level.‘;
????elseif?h1==1
????????RESULTS.WilcoxonComment =?‘Th

?屬性????????????大小?????日期????時間???名稱
-----------?---------??----------?-----??----

?????文件???????4689??2010-09-15?17:18??msvr-2-1\assessment.m

?????文件???????2213??2018-12-31?22:17??msvr-2-1\demoMSVR.m

?????文件???????1642??2010-09-15?17:18??msvr-2-1\kernelmatrix.m

?????文件???????3312??2016-02-16?15:39??msvr-2-1\msvr.m

?????文件????????198??2010-09-15?17:18??msvr-2-1\scale.m

?????目錄??????????0??2018-12-31?22:23??msvr-2-1

-----------?---------??----------?-----??----

????????????????12054????????????????????6


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