The Simple Perceptron I 5 W =1 dendrite:input; axon:output 5166
The Simple Perceptron I o = fact( P 5 n=1 in · wn) dendrite: input; axon: output 5 / 66
Threshold Logic Unit (TLU) inputs N weights activation output W2 X ∑ o n a= WiXi y= {0 ,ifa≥0 otherwise 6/66
Threshold Logic Unit (TLU) a = P n i=1 wixi y = n 1 , if a ≥ θ 0 , otherwise 6 / 66
Outline (Level 1) Preparatory knowledge ②Perceptron concept Learning strategies of perceptron Perceptron learning algorithm Convergence of perceptron learning algorithm Dual form of perceptron learning algorithm 7166
Outline (Level 1) 1 Preparatory knowledge 2 Perceptron concept 3 Learning strategies of perceptron 4 Perceptron learning algorithm 5 Convergence of perceptron learning algorithm 6 Dual form of perceptron learning algorithm 7 / 66
2.Perceptron concept Perceptron is a linear binary classification model. 1 Input is the feature vector of the training sample 2 output is the category of the sample,taking +1 and-1 3 a hyper-plane in input space(feature space),dividing samples into positive and negative types 4 a discriminative model Linear regression model:Output is continuous Perceptron:Output is discrete 8/66
2. Perceptron concept ▶ Perceptron is a linear binary classification model. 1 Input is the feature vector of the training sample 2 output is the category of the sample, taking +1 and −1 3 a hyper-plane in input space (feature space), dividing samples into positive and negative types 4 a discriminative model ▶ Linear regression model: Output is continuous ▶ Perceptron: Output is discrete 8 / 66
Outline (Level 1-2) Perceptron concept Definition of Perceptron o Geometric interpretation of perceptron 9166
Outline (Level 1-2) 2 Perceptron concept Definition of Perceptron Geometric interpretation of perceptron 9 / 66