第一章
target vector t
generalization: Once the model is trained it can then determine the identity of new digit images, which are said to comprise a test set, the ability to categorize correctly new examples that differ from those used for training is known as generalization.
feature extraction: For most practical applications, the original input variables are typically preprocessed to transofrm them into some new space of variables where, it is hoped, the pattern recognition problem will be easier to solve. This pre-processing stage is sometimes also called feature extraction.
supervised learning problem: Applications in which the training data comprises examples of the input vectors along with their corresponding
classification:
regression
unsupervised learning
clustering
density estimation
visualization
reinforcement learning
exploration
exploitation