機(jī)器視覺技術(shù)與應(yīng)用

1、Logistic Regression,Classification,Machine Learning,Classification,Email: Spam / Not Spam? Online Transactions: Fraudulent (Yes / No)? Tumor: Malignant / Benign ?,0: “Negative Class” (e.g., benign tumor) 1: “Positive Class” (e.g., malignant tumor),Tumor Size,Threshold classifier output at 0.5:,If ,

2、predict “y = 1”,If , predict “y = 0”,Tumor Size,Malignant ?,(Yes) 1,(No) 0,Classification: y = 0 or 1,can be 1 or 0,Logistic Regression:,Logistic Regression,HypothesisRepresentation,Machine Learning,Sigmoid function Logistic function,Logistic Regression Model,Want,0,Interpretation of Hypothesis Outp

3、ut,= estimated probability that y = 1 on input x,Tell patient that 70% chance of tumor being malignant,Example: If,“probability that y = 1, given x, parameterized by ”,Logistic Regression,Decision boundary,Machine Learning,Logistic regression,Suppose predict “ “ if,predict “ “ if,x1,x2,Decision Boun

4、dary,1,2,3,1,2,3,Predict “ “ if,Non-linear decision boundaries,x1,x2,Predict “ “ if,x1,x2,1,-1,-1,1,Logistic Regression,Cost function,Machine Learning,Training set:,How to choose parameters ?,m examples,Cost function,Linear regression:,“non-convex”,“convex”,Logistic regression cost function,If y = 1

5、,1,0,Logistic regression cost function,If y = 0,1,0,Logistic Regression,Simplified cost function and gradient descent,Machine Learning,Logistic regression cost function,Output,Logistic regression cost function,To fit parameters :,To make a prediction given new :,Gradient Descent,Want :,Repeat,(simul

6、taneously update all ),Gradient Descent,Want :,(simultaneously update all ),Repeat,Algorithm looks identical to linear regression!,Logistic Regression,Advanced optimization,Machine Learning,Optimization algorithm,Cost function . Want .,Given , we have code that can compute,(for ),Repeat,Gradient des

7、cent:,Optimization algorithm,Given , we have code that can compute,(for ),Optimization algorithms: Gradient descent,Conjugate gradient BFGS L-BFGS,Advantages: No need to manually pick Often faster than gradient descent. Disadvantages: More complex,Example:,function jVal, gradient = costFunction(thet

8、a) jVal = (theta(1)-5)2 + . (theta(2)-5)2; gradient = zeros(2,1); gradient(1) = 2*(theta(1)-5);gradient(2) = 2*(theta(2)-5);,options = optimset(GradObj, on, MaxIter, 100); initialTheta = zeros(2,1);optTheta, functionVal, exitFlag . = fminunc(costFunction, initialTheta, options);,gradient(1) = ;,func

9、tion jVal, gradient = costFunction(theta),theta =,jVal = ;,gradient(2) = ;,gradient(n+1) = ;,code to compute,code to compute,code to compute,code to compute,Logistic Regression,Multi-class classification: One-vs-all,Machine Learning,Multiclass classification,Email foldering/tagging: Work, Friends, Family, Hobby,Medical diagrams: Not ill, Cold, Flu,Weather: Sunny, Cloudy, Rain, Snow,x1,x2,Binary classification:,Multi-class classification:,x1,x2,One-vs-all (one-vs-rest):,Class 1: Class 2: Class 3:,x1,x2,x1,x2,x1,x2,One-vs-all,Tra