5-1 Intro. to PR


This section introduces several basic methods in pattern recognition (PR for short), which tries to construct a classifier or recognizer that can classify unseen data based on a given dataset with known classes. In general, we have two datasets for PR:

The general approach to PR involves the following steps:
  1. Data collection and feature selection
  2. Divide the data into training and test sets
  3. Select a method to construct the classifier based on the training set
  4. Verify the performance of the method based on the test set
  5. If the performance is satisfactory, stop. Otherwise, go back to step 3 or step 1.

Data Clustering and Pattern Recognition (資料分群與樣式辨認)