# Pattern Recognition | Basics and Design Principles

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
- Differentiate between good and bad features.
- Feature properties.
- 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. **Classifier**is used to partition the feature space into class-labeled decision regions. While**Decision Boundaries**are the borders between decision regions.**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.- Statistical Approach and
- Structural Approach
**Descriptive Statistics:**It summarizes data from a sample using indexes such as the mean or standard deviation.**Inferential Statistics:**It draw conclusions from data that are subject to random variation.- Sentence Patterns
- Phrase Patterns
- Formulas
- Idioms

**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:**

**Classifier and Decision Boundaries:**

**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:

**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 different techniques. These are –

**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 :

**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:

**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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