Lecture10 - 12/17/2009 Data Mining: Principles and...

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Unformatted text preview: 12/17/2009 Data Mining: Principles and Algorithms 1 Data Mining: Principles and Algorithms Jianyong Wang Database Lab, Institute of Software Department of Computer Science and Technology Tsinghua University jianyong@tsinghua.edu.cn 12/17/2009 Data Mining: Principles and Algorithms 2 Chapter 6. Classification and Prediction What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Artificial Neural Networks Support Vector Machines (SVM) Associative classification Lazy learners (or learning from your neighbors) Other classification methods Ensemble methods Prediction Accuracy and error measures Summary 12/17/2009 Data Mining: Principles and Algorithms 3 Artificial Neural Networks Composed of basic units and weighted links between them The basic units (or nodes) are an idealization of neurons- Responsible for basic computations The pattern of connections of the units determines the network architecture 12/17/2009 Data Mining: Principles and Algorithms 4 Artificial Neural Networks X 1 X 2 X 3 Y 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 X 1 X 2 X 3 Y Black box Output Input Output Y is 1 if at least two of the three inputs are equal to 1. 12/17/2009 Data Mining: Principles and Algorithms 5 Artificial Neural Networks X 1 X 2 X 3 Y 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 X 1 X 2 X 3 Y Black box 0.3 0.3 0.3 t=0.4 Output node Input nodes otherwise true is if 1 ) ( where ) 4 . 3 . 3 . 3 . ( 3 2 1 z z I X X X I Y 12/17/2009 Data Mining: Principles and Algorithms 6 Artificial Neural Networks Model is an assembly of inter-connected nodes and weighted links Output node sums up each of its input value according to the weights of its links Compare output node against some threshold t X 1 X 2 X 3 Y Black box w 1 t Output node Input nodes w 2 w 3 ) ( t X w I Y i i i Perceptron Model ) ( t X w sign Y i i i or 12/17/2009 Data Mining: Principles and Algorithms 7 General Structure of ANN Activation function g(S i ) S i O i I 1 I 2 I 3 w i1 w i2 w i3 O i Neuron i Input Output threshold, t Input Layer Hidden Layer Output Layer x 1 x 2 x 3 x 4 x 5 y Training ANN means learning the weights of the neurons 12/17/2009 Data Mining: Principles and Algorithms 8 Common Activation Functions Step function: - g(x)=1, if x >= t ( t is a threshold)- g(x) = 0, if x < t Sign function: - g(x)=1, if x >= t ( t is a threshold)- g(x) = -1, if x < t Sigmoid function: g(x)= 1/(1+exp(-x)) 12/17/2009 Data Mining: Principles and Algorithms 9 Network Structures Single-layered network vs. multi-layered network - Perceptron : contains only input and output nodes (but no hidden layer) Basically linear threshold functions (ltf): defined by weights W and threshold t , value is 1 iff W × x ≥ t , otherwise, 0 Applying model is straightforward, e.g., Y=step(0.3XApplying model is straightforward, e....
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Lecture10 - 12/17/2009 Data Mining: Principles and...

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