tessellations
polyhedrons
pattern recognition is especially good in learning to read
Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities. The Bayesian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with propositions whose truth or falsity is uncertain. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data.
using pattern recognition when illustrating and writing our adventure hero
comic book : use big hair, ponytails, eyelashes, earrings to identify
female characters
Commonly Used Terminology in Pattern Recognition
A set of variables believed to carry discriminating and characterizing
information about an object to be identified are called features :
A collection of d such features, ordered in some meaningful way into
d-dimensional column vector is the feature vector :
category to which a given object belongs : class or label :
collection of features of an object under consideration, along with the
correct class information for that object, is then called a pattern :
pattern of an object is also referred to as an instance or an exemplar :
goal of pattern recognition is to estimate the correct label corresponding
to a given feature vector based on knowledge obtained through training.
Training is the procedure by which the pattern recognition system learns the mapping relationship between feature vectors and their corresponding labels.
This relationship forms the decision boundary in the d-dimensional feature space that separates patterns of different classes from each other.
goal of pattern recognition algorithm : determine decision boundaries :
which are nonlinear functions :
Consequently, pattern recognition can also be cast as a function approximation problem.
alogrithm : culling
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