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ECE471-571 – Pattern Recognition Midterm Review

ECE471-571 – Pattern Recognition Midterm Review. Hairong Qi, Gonzalez Family Professor Electrical Engineering and Computer Science University of Tennessee, Knoxville http://www.eecs.utk.edu/faculty/qi Email: hqi@utk.edu. Pattern Classification. Statistical Approach.

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ECE471-571 – Pattern Recognition Midterm Review

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  1. ECE471-571 – Pattern RecognitionMidtermReview Hairong Qi, Gonzalez Family Professor Electrical Engineering and Computer Science University of Tennessee, Knoxville http://www.eecs.utk.edu/faculty/qi Email: hqi@utk.edu

  2. Pattern Classification Statistical Approach Non-Statistical Approach Supervised Unsupervised Decision-tree Basic concepts: Baysian decision rule (MPP, LR, Discri.) Basic concepts: Distance Agglomerative method Syntactic approach Parameter estimate (ML, BL) k-means Non-Parametric learning (kNN) Winner-takes-all LDF (Perceptron) Kohonen maps NN (BP) Mean-shift Support Vector Machine Deep Learning (DL) Dimensionality Reduction FLD, PCA Performance Evaluation ROC curve (TP, TN, FN, FP) cross validation Stochastic Methods local opt (GD) global opt (SA, GA) Classifier Fusion majority voting NB, BKS

  3. Q&A • What is the common feature of statistical pattern classification? • What is supervised pattern classification? How is it different from unsupervised classification? • When performing classification, what is the general rule that Baysian decision methods follow? (MPP, Discriminant function) • What are the differences between the three cases of discriminant function? • What’s the difference between Euclidean distance and Mahalanobis distance? • What is the difference between parametric classification and non-parametric classification? • Understand behavior/characteristics of m-D Gaussian • What is maximum likelihood estimation? • Both FLD and PCA reduce the dimension of a dataset. However, different projection directions are generated from the two approaches. Comment on the fundamental differences. • How to quantify error in representation in PCA?

  4. Q&A • Can you verbally describe kNN? And possible improvements? • Performance evaluation • Learn to plot and use and explain an ROC curve • Be familiar with the relationship between TP, FP, TN, FN, precision, recall, specificity, sensitivity, and accuracy • Whatiscrossvalidation?

  5. Forming Objective Functions • Maximum a-posteriori probability • Maximum likelihood estimate • Fisher’s linear discriminant • Principal component analysis • kNN • ROC curve

  6. Distance Metrics • Euclidean distance • Mahalanobis distance • Minkowski distance • Manhattan distance (city-block distance) • Euclidean distance

  7. Review Materials • HW1, 2 • Proj 1,2 • Lectures 1-10

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