fisher准则线性分类器设计

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1、、基于Fisher准则线性分类器设计1、实验内容：已知有两类数据叫和仍2二者的概率已知p（叫=0.6，p⑽2=0.4。eo｝中数据点的坐标对应如下：数据：X=0.23311.52070.64990.77571.05241.19740.29080.25180.66820.56220.90230.1333-0.54310.9407-0.21260.0507-0.08100.73150.33451•0650-0.02470.10430.31220.66550.58381.16531.26530.8137-0.33990.51520.7226-0.20150.40

2、70-0.1717-1.0573-0.2099y2.33852.19461.67301.63651.78442.01552.06812.12132.47971.51181.96921.83401.87042.29481.77142.39391.56481.93292.20272.45681.75231.69912.48831.72592.04662.02262.37571.79872.08282.07981.94492.38012.23732.16141.92352.2604Z—0.53380.85141.08310.41641.11760.55360.6

3、0710.44390.49280.59011.09271.07561.00720.42720.43530.98690.48411.09921.02990.71271.01240.45760.85441.12750.77050.41291.00850.76760.84180.87840.97510.78400.41581.03150.75330.9548eo2数据点的对应的三维坐标为x2=1.40101.23012.08141.16551•37401.18291.76321.97392.41522.58902.84721.95391.25001.28641.

4、26142.00712.18311.79091.33221.14661.70871.59202.93531.46642.93131.83491.83402.50962.71982.31482.03532.60301.23272.14651.56732.94141.02980.96110.91541.49010.82000.93991.14051.06780.80501.28891.46011.43340.70911.29421.37440.93871.22661.18330.87980.55920.51500.99830.91200.71261.28331

5、.10291.26800.71401.24461.33921.18080.55031.47081.14350.76791.1288z2=0.62101.36560.54980.67080.89321.43420.95080.73240.57841.49431.09150.76441.21591•30491.14080.93980.61970.66031.39281.40840.69090.84000.53811.37290.77310.73191.34390.81420.95860.73790.75480.73930.67390.86511.36991.1

6、458数据的样本点分布如下图:1)请把数据作为样本，根据Fisher选择投影方向VV的原则，使原样本向量在该方向上的投影能兼顾类间分布尽可能分开，类内样木投影尽可能密集的要求，求出评价投影方向VV的函数，并在图形表示出来。并在实验报告中表示出来，并求使JF(w)取极大值的用matlab完成Fisher线性分类器的设计，程序的语句要求有注释。2)根据上述的结果并判断(1，1.5，0.6)(1.2,1.0，0.55)，(2.0,0.9,0.68)，(1.2,1.5,0.89),(0.23,2.33,1.43),属于哪个类别，并画山数据分类相应的结果图，要求画出

7、其在IV上的投影。3)回答如下问题，分析一下W的比例因子对于Fisher判别函数没有影响的原因。2、实验代码xl=[0.23311.52070.64990.77571.05241.19740.29080.25180.66820.56220.90230.1333-0.54310.9407-0.21260.0507•0.08100.73150.33451.0650-0.02470.10430.31220.66550.58381.16531.26530.8137-0.33990.51520.7226-0.20150.4070-0.1717•1.0573-0.20

8、99];x2=[2.33852.19461.67301.63651