III WVC 2007 - Iris.sel.eesc.sc.usp.br - USP

WVC'2007 - III Workshop de Visão Computacional, 22 a 24 de Outubro de 2007, São José do Rio Preto, SP.A)ANEXODerivação da regra de aprendizagem para os neurônios da camada de saída da rede A∂E∂E∂Y2 ∂I2∇ E = = * *∂W2 ∂Y2 ∂I2 ∂W2AjiAjiAjAjAjAjAjiA∂ I 2(2)j A= Y1i∂W2A∂ Y 2 (3)jA= G`(I 2 j )A∂I2 j⎛A B∂⎞( ) ⎜Y 2 j −Y2(4)Ej= − δ ( α,β ) − d ( α,β ) *⎟A AB∂Y2 ⎜ ( α,β ) ⎟jd⎝AB⎠Substituindo (2), (3) e (4) em (1):A BE⎛ Y 2A A( )j Y 2 jE ( , ) dA AB ( , ) ** G`(I 2 j )* Y1iW 2 jid AB ( , ) ⎟ ⎟ ⎞⎜−(5)∂∇ = = − δ α β − α β∂⎜ α β⎝⎠No qual:⎛A BY 2( )j Y 2 j⎞A A2 j ( , ) d ( , ) * ⎜−(6)ρ = δ α β − AB α β⎟*G`(I 2 j )⎜ d AB ( α,β ) ⎟⎝⎠Ajuste ao gradiente:AA A∆ W 2 = −η * ∇E= η * ρ2* Y1(7)AjijiAjijAjiAiW 2 ( t + 1) = W 2 ( t)+η * ρ2* Y1(8)(1)B)Pontos obtidos pelo método MDS clássico no exemplo de cores de Ekman [10]Cor Dim1 Dim2 Dim3 Dim4 Dim5 Dim6 Dim7 Dim8 Dim9 Dim10 Dim11 Dim12 Dim13434 0.223 -0.419 0.236 -0.179 0.087 0.004 0.014 -0.026 -0.014 -0.033 0.042 -0.019 -0.002445 0.263 -0.412 0.202 -0.171 0.045 0.005 -0.021 0.027 0.014 0.026 -0.036 0.016 0.001465 0.414 -0.312 -0.065 0.174 -0.115 -0.075 -0.001 0.03 0.041 0.053 -0.03 -0.004 0.003472 0.435 -0.274 -0.114 0.189 -0.051 -0.016 0.023 -0.027 -0.04 -0.048 0.036 -0.001 -0.003490 0.425 0.083 -0.287 0.122 0.112 0.06 -0.063 0 0.008 0.012 -0.013 -0.002 0.001504 0.326 0.382 -0.208 -0.066 0.164 0.04 0.048 0.01 -0.009 -0.01 0.003 0.005 -0.002537 0.239 0.483 0.046 -0.193 -0.014 -0.097 0.061 -0.001 0.026 0.004 0.02 0 0.001555 0.194 0.485 0.149 -0.121 -0.168 -0.044 -0.094 0.002 -0.032 -0.005 -0.027 -0.002 -0.007584 -0.225 0.28 0.272 0.244 -0.104 0.209 0.012 0.024 0.021 -0.028 -0.014 0 -0.011600 -0.398 0.139 0.187 0.229 0.084 -0.093 -0.001 0.013 -0.006 0.006 0.014 -0.001 -0.004610 -0.496 -0.028 0.011 0.119 0.085 -0.078 0.017 -0.037 0 0.005 0.007 0.001 0.009628 -0.503 -0.103 -0.106 -0.052 0.034 -0.038 -0.022 0.038 -0.005 -0.014 -0.001 0.003 0.009651 -0.467 -0.142 -0.17 -0.127 -0.048 -0.017 -0.013 0.045 -0.005 0.04 0.008 0.003 -0.008674 -0.431 -0.161 -0.152 -0.17 -0.112 0.141 0.041 -0.099 0.001 -0.008 -0.009 0 0.012145