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Pattern Recognition | Basics and Design Principles
• Difficulty Level : Medium
• Last Updated : 05 Sep, 2019

Prerequisite – Pattern Recognition | Introduction
Pattern Recognition System
Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms.

In Pattern Recognition, pattern is comprises of the following two fundamental things:

• Collection of observations
• The concept behind the observation
• Feature Vector:
The collection of observations is also known as a feature vector. A feature is a distinctive characteristic of a good or service that sets it apart from similar items. Feature vector is the combination of n features in n-dimensional column vector.The different classes may have different features values but the same class always has the same features values.

Example:

• Differentiate between good and bad features.
• Feature properties.
• Classifier and Decision Boundaries:

1. In a statistical-classification problem, a decision boundary is a hypersurface that partitions the underlying vector space into two sets. A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous.Classifier is a hypothesis or discrete-valued function that is used to assign (categorical) class labels to particular data points.
2. Classifier is used to partition the feature space into class-labeled decision regions. While Decision Boundaries are the borders between decision regions.
3. Components in Pattern Recognition System:
A pattern recognition systems can be partitioned into components.There are five typical components for various pattern recognition systems. These are as following:

• A Sensor : A sensor is a device used to measure a property, such as pressure, position, temperature, or acceleration, and respond with feedback.
• A Preprocessing Mechanism : Segmentation is used and it is the process of partitioning a data into multiple segments. It can also be defined as the technique of dividing or partitioning an data into parts called segments.
• A Feature Extraction Mechanism : feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations. It can be manual or automated.
• A Description Algorithm : Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to perform “most likely” matching of the inputs, taking into account their statistical variation
• A Training Set : Training data is a certain percentage of an overall dataset along with testing set. As a rule, the better the training data, the better the algorithm or classifier performs.

Design Principles of Pattern Recognition
In pattern recognition system, for recognizing the pattern or structure two basic approaches are used which can be implemented in diferrent techniques. These are –

• Statistical Approach and
• Structural Approach

Statistical Approach:
Statistical methods are mathematical formulas, models, and techniques that are used in the statistical analysis of raw research data. The application of statistical methods extracts information from research data and provides different ways to assess the robustness of research outputs.

Two main statistical methods are used :

1. Descriptive Statistics: It summarizes data from a sample using indexes such as the mean or standard deviation.
2. Inferential Statistics: It draw conclusions from data that are subject to random variation.

Structural Approach:
The Structural Approach is a technique wherein the learner masters the pattern of sentence. Structures are the different arrangements of words in one accepted style or the other.

Types of structures:

• Sentence Patterns
• Phrase Patterns
• Formulas
• Idioms

Difference Between Statistical Approach and Structural Approach:

Sr. No. Statistical Approach Structural Approach
1 Statistical decision theory. Human perception and cognition.
2 Quantitative features. Morphological primitives
3 Fixed number of features. Variable number of primitives.
4 Ignores feature relationships. Captures primitives relationships.
5 Semantics from feature position. Semantics from primitives encoding.
6 Statistical classifiers. Syntactic grammars.

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