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1、。模式识别第三章感知器算法一.用感知器算法求下列模式分类的解向量w:1:{(0,0,0)T,(1,0,0)T,(1,0,1)T,(1,1,0)T}2:{(0,0,1)T,(0,1,1)T,(0,1,0)T,(1,1,1)T}将属于2的训练样本乘以(-1),并写成增广向量的形式:x1(0,0,0,1)T,x2(1,0,0,1)T,x3(1,0,1,1)T,x4(1,1,0,1)Tx5(0,0,-1,1)T,x6(0,-1,-1,1)T,x7(0,-1,0,1)T,x8(-1,-1,-1,1)T第一轮迭代:取C,(1)(
2、0,0,0,0)T1因T(1)x1(0,0,0,0)(0,0,0,1)T0不大于0,故(2)(1)x1(0,0,0,1)T因T(2)x2(0,0,0,1)(1,0,0,1)T1大于0,故(3)(2)(0,0,0,1)T因T(3)x3(0,0,0,1)(1,0,1,1)T1大于0,故(4)(3)(0,0,0,1)T因T(4)x4(0,0,0,1)(1,1,0,1)T1大于0,故(5)(4)(0,0,0,1)T因T(5)x5(0,0,0,1)(0,0,-1,1)T1不大于0,故(6)(5)x5(0,0,-1,0)T因T(
3、6)x6(0,0,-1,0)(0,-1,-1,1)T1大于0,故(7)(6)(0,0,-1,0)T因T(7)x7(0,0,-1,0)(0,-1,0,1)T0不大于0,故(8)(7)x7(0,1,-1,-1)T因T(8)x8(0,1,-1,-1)(-1,-1,-1,1)T3大于0,故(9)(8)(0,1,-1,-1)T第二轮迭代:因T(9)x1(0,1,-1,-1)(0,0,0,1)T1不大于0,故(10)(9)x1(0,1,-1,0)T因T(10)x2(0,-1,-1,0)(1,0,0,1)T0不大于0,故(11)(
4、10)x2(1,1,1,1)T-可编辑修改-。因T(11)x3(1,1,1,1)(1,0,1,1)T1大于0,故(12)(11)(1,1,1,1)T因T(12)x4(1,1,1,1)(1,1,0,1)T1大于0,故(13)(12)(1,1,1,1)T因T(13)x5(1,1,1,1)(0,0,-1,1)T0不大于0,故(14)(13)x5(1,1,2,0)T因T(14)x6(1,1,2,0)(0,-1,-1,1)T3大于0,故(15)(14)(1,1,2,0)T因T(15)x7(1,1,2,0)(0,-1,0,1)T
5、1大于0,故因T(16)x8(1,-1,-2,0)(-1,-1,-1,1)T2大于0,故(16)(15)(1,-1,-2,0)T(17)(16)(1,-1,-2,0)T第三轮迭代:因T(17)x1(1,1,-2,0)(0,0,0,1)T0不大于0,故(18)(17)x1(1,1,2,1)T因T(18)x2(1,-1,-2,1)(1,0,0,1)T2大于0,故(19)(18)(1,1,2,1)T因T(19)x3(1,1,2,1)(1,0,1,1)T0不大于0,故(20)(19)x3(2,1,1,2)T因T(20)x4
6、(2,1,1,2)(1,1,0,1)T3大于0,故(21)(20)(2,1,1,2)T因T(21)x5(2,1,1,2)(0,0,-1,1)T1不大于0,故(22)(21)x5(2,1,2,1)T因T(22)x6(2,1,2,1)(0,-1,-1,1)T2大于0,故(23)(22)(2,1,2,1)T因T(23)x7(2,1,2,1)(0,-1,0,1)T0不大于0,故(24)(23)x7(2,-2,-2,0)T因T(24)x8(2,-2,-2,0)(-1,-1,-1,1)T2大于0,故(25)(24)(2,-2,-
7、2,0)T第四轮迭代:因T(25)x1(2,2,-2,0)(0,0,0,1)T0不大于0,故(26)(25)x1(2,2,2,1)T因T(26)x2(2,-2,-2,1)(1,0,0,1)T3大于0,故因T(27)x3(2,2,2,1)(1,0,1,1)T1大于0,故因T(28)x4(2,2,2,1)(1,1,0,1)T1大于0,故(27)(26)(2,2,2,1)T(28)(27)(2,2,2,1)T(29)(28)(2,2,2,1)T因T(29)x5(2,2,2,1)(0,0,-1,1)T1大于0,故(30)(
8、29)(2,2,2,1)T因T(30)x6(2,2,2,1)(0,-1,-1,1)T3大于0,故(31)(20)(2,2,2,1)T-可编辑修改-。因T(31)x7(2,2,2,1)(0,-1,0,1)T1大于0,故(32)(31)(2,-2,-2,1)T因大于0,故(33)(32)(2,-2,-2,1)T第五轮迭代:因T(33)x1(2,2,