GeneralizingLinearClassification假设我们有如上图的trainingdata,注意到此时\(\mathcal{X}\subset\mathbb{R}^{2}\)。那么decisionboundary\(g\):\[g(\vec{x})=w_{1}x_{1}^{2}+w_{2}x_{2}^{2}+w_{0}\]即,decisionboundary为某种椭圆,例如:半径为\(r\)的圆(\(w_{1}=1,w_{2}=1,w_{0}=-r^{2}\)),如上图中的黑圈所示。我们会发现,此时decisionboundarynotlinearin\(\vec{x}\)。但
GeneralizingLinearClassification假设我们有如上图的trainingdata,注意到此时\(\mathcal{X}\subset\mathbb{R}^{2}\)。那么decisionboundary\(g\):\[g(\vec{x})=w_{1}x_{1}^{2}+w_{2}x_{2}^{2}+w_{0}\]即,decisionboundary为某种椭圆,例如:半径为\(r\)的圆(\(w_{1}=1,w_{2}=1,w_{0}=-r^{2}\)),如上图中的黑圈所示。我们会发现,此时decisionboundarynotlinearin\(\vec{x}\)。但
Theperceptronalgorithmanditsmistakebound.
Theperceptronalgorithmanditsmistakebound.