File Name: pattern recognition and application .zip
Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. Example: The colours on the clothes, speech pattern etc.
Pattern is everything around in this digital world. A pattern can either be seen physically or it can be observed mathematically by applying algorithms. Example: The colours on the clothes, speech pattern etc. In computer science, a pattern is represented using vector features values. Pattern recognition is the process of recognizing patterns by using machine learning algorithm.
One of the important aspects of the pattern recognition is its application potential. Examples: Speech recognition, speaker identification, multimedia document recognition MDR , automatic medical diagnosis. In a typical pattern recognition application, the raw data is processed and converted into a form that is amenable for a machine to use.
Pattern recognition involves classification and cluster of patterns. Features may be represented as continuous, discrete or discrete binary variables. A feature is a function of one or more measurements, computed so that it quantifies some significant characteristics of the object.
Example: consider our face then eyes, ears, nose etc are features of the face. A set of features that are taken together, forms the features vector. Example: In the above example of face, if all the features eyes, ears, nose etc taken together then the sequence is feature vector [eyes, ears, nose].
Feature vector is the sequence of a features represented as a d-dimensional column vector. Sequence of first 13 features forms a feature vector. Learning is a phenomena through which a system gets trained and becomes adaptable to give result in an accurate manner.
Learning is the most important phase as how well the system performs on the data provided to the system depends on which algorithms used on the data. Entire dataset is divided into two categories, one which is used in training the model i. Training set and the other that is used in testing the model after training, i. Testing set. Real-time Examples and Explanations: A pattern is a physical object or an abstract notion. While talking about the classes of animals, a description of an animal would be a pattern.
While talking about various types of balls, then a description of a ball is a pattern. In the case balls considered as pattern, the classes could be football, cricket ball, table tennis ball etc. Given a new pattern, the class of the pattern is to be determined. The choice of attributes and representation of patterns is a very important step in pattern classification.
A good representation is one which makes use of discriminating attributes and also reduces the computational burden in pattern classification.
An obvious representation of a pattern will be a vector. Each element of the vector can represent one attribute of the pattern.
The first element of the vector will contain the value of the first attribute for the pattern being considered. Example: While representing spherical objects, 25, 1 may be represented as an spherical object with 25 units of weight and 1 unit diameter.
The class label can form a part of the vector. If spherical objects belong to class 1, the vector would be 25, 1, 1 , where the first element represents the weight of the object, the second element, the diameter of the object and the third element represents the class of the object. Writing code in comment? Please use ide. Skip to content. Related Articles. Last Updated : 04 May, What is Pattern Recognition?
In classification, an appropriate class label is assigned to a pattern based on an abstraction that is generated using a set of training patterns or domain knowledge. Classification is used in supervised learning. Clustering generated a partition of the data which helps decision making, the specific decision making activity of interest to us.
Clustering is used in an unsupervised learning. Pattern recognition possesses the following features:. Recommended Articles. How to use built-in image classifiers of visual recognition module using IBM watson?
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Recent advances in data acquisition and various monitoring modalities have resulted in generating and collecting a growing volume of biological and medical data at unprecedented speed and scale [ 1 ]. These accumulated data can be utilized for a more effective delivery of care and enhanced clinical decision-making [ 2 ]. However, analysis of these tremendous amounts of data—collected from electronic health records or monitoring devices—to extract useful information for a more broad-based health-care delivery is one of the main challenges of today's medicine [ 3 ]. Medical decision support systems help clinicians to best exploit these overwhelming amount of data by providing a computerized platform for integrating evidence-based knowledge and patient-specific information into an enhanced and cost-effective health care [ 4 ]. Over the last decade, various pattern recognition techniques have been applied to biomedical data including signals and images for automatic and machine-based clinical diagnostic and therapeutic support. This is of vital importance especially when a rapid clinical decision needs to be made in a stressful environment such as intensive care units [ 5 ].
Once production of your article has started, you can track the status of your article via Track Your Accepted Article. Help expand a public dataset of research that support the SDGs. Pattern Recognition is a mature but exciting and fast developing field, which underpins developments in cognate fields such as computer vision, image processing, text and document analysis and neural networks. It is closely akin to machine learning, and also finds applications in fast emerging areas It is closely akin to machine learning, and also finds applications in fast emerging areas such as biometrics, bioinformatics, multimedia data analysis and most recently data science.
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It seems that you're in Germany. We have a dedicated site for Germany. The book provides a comprehensive view of Pattern Recognition concepts and methods, illustrated with real-life applications in several areas e.
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