01/03
Statistical pattern recognition systems rely on statistical methods to analyze and classify patterns within data. These methods involve quantifying the likelihood of a pattern belonging to a particular class based on its statistical properties.
Bayesian Classification: Maximum Likelihood Estimation (MLE): Support Vector Machines (SVM):
Utilizes Bayes' theorem to calculate the probability of a pattern belonging to each class based on observed data.
Determines the parameters of a statistical model that best explain the observed data.
Constructs hyperplanes in a high-dimensional space to separate different classes of patterns.
02/03
Structural pattern recognition systems focus on analyzing the structural relationships between different components of patterns. These systems are particularly useful when dealing with patterns that can be represented hierarchically or through a symbolic structure.
Grammars and Parsing: Graph Matching: Syntactic Pattern Recognition:
Utilizes formal grammar to describe the syntax of patterns and parsing algorithms to analyze and recognize them.
Matches patterns by comparing their structural representations as graphs.
Uses rules or templates to describe the structure of patterns and match them accordingly.
03/03
Neural pattern recognition systems are inspired by the structure and function of the human brain. These systems use artificial neural networks to learn and recognize patterns from data. Neural networks consist of interconnected nodes (neurons) organized in layers, where each neuron processes input data and passes the result to the next layer.
Deep Learning: Convolutional Neural Networks (CNNs): Recurrent Neural Networks (RNNs):
Utilizes deep neural networks with multiple hidden layers to learn hierarchical representations of data.
Specialized neural networks for processing grid-like data, such as images, by applying convolutional operations.
Designed to handle sequential data by maintaining internal memory states.