pattern recognition overview

Assign an unknown pattern to one of several
known categories (or classes).
How do we model a Pattern Class?
• Typically, using a statistical model.
– probability density function (e.g., Gaussian)

Key challenges:
– Intra-class variability
– Inter-class variability

Pattern Recognition:
Main Objectives
• Hypothesize the models that describe each
pattern class (e.g., recover the process that
generated the patterns).
• Given a novel pattern, choose the best-fitting
model for it and then assign it to the pattern class
associated with the model.

Classification vs Clustering
– Classification (known categories)
– Clustering (unknown categories)

Main Classification Approaches
• Generative
x: input vector (pattern)
y: class label (class)
– Model the joint probability, p(x, y)
– Make predictions by using Bayes rules to calculate p(ylx)
– Pick the most likely label y
• Discriminative
– Estimate p(ylx) directly (e.g., learn a direct map from inputs x to
the class labels y)
– Pick the most likely label y

Feature Extraction
• Assume a fisherman told us that a sea bass is
generally longer than a salmon.
• We can use length as a feature and decide
between sea bass and salmon according to a
threshold on length.