ECE 471/571 – Review 1.

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Presentation transcript:

ECE 471/571 – Review 1

A Roadmap 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-take-all LDF (Perceptron) Kohonen maps NN (BP, Hopfield, DL) Support Vector Machine 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

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?

Forming Objective Functions Maximum a-posteriori probability Maximum likelihood estimate Fisher’s linear discriminant Principal component analysis

Distance Metrics Euclidean distance Mahalanobis distance

Review materials HW1, 2 Proj 1 Lectures 1-7