In a very simple language, Pattern Recognition is a type of problem while Machine Learning is a type of solution. Pattern recognition is closely related to artificial intelligence and machine learning. Pattern Recognition is an engineering application of Machine Learning.

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2. Machine Learning and Pattern Recognition

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Machine Learning and Pattern Recognition:
Machine Learning and Pattern Recognition: In a very simple language, Pattern
Recognition is a type of problem while Machine Learning is a type of solution.
Pattern recognition is closely related to artificial intelligence and machine learning.
Pattern Recognition is an engineering application of Machine Learning. Machine
learning deals with the construction and study of systems that can learn from data,
rather than follow only explicitly programmed instructions whereas Pattern
recognition is the recognition of patterns and regularities in data.

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1.Machine Learning :
The goal of Machine learning is never to make ‘perfect’ guesses because Machine
Learning deals in domains where there is no such thing. The goal is to make guesses
that are good enough to be useful. Machine learning is a method of data analysis that
automates analytical model building. Machine learning is a field that uses algorithms
to learn from data and make predictions. A machine-learning algorithm then takes
these examples and produces a program that does the job. Machine Learning builds
heavily on statistics. For example, when we train our machine to learn, we have to give
it a statistically significant random sample as training data. If the training set is not
random, we run the risk of the machine learning patterns that aren’t actually there.

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2. Pattern Recognition:
Pattern recognition is the process of recognizing patterns by using a machine learning
algorithm. Pattern recognition can be defined as the classification of data based on
knowledge already gained or on statistical information extracted from patterns and/or
their representation. Pattern recognition is the ability to detect arrangements of
characteristics or data that yield information about a given system or data set.
Predictive analytics in data science work can make use of pattern recognition
algorithms to isolate statistically probable movements of time series data into the
future. In a technological context, a pattern might be recurring sequences of data over
time that can be used to predict trends, particular configurations of features in images
that identify objects, frequent combinations of words and phrases for natural language
processing (NLP), or particular clusters of behaviour on a network that could indicate
an attack — among almost endless other possibilities

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In IT, pattern recognition is a branch of machine learning that emphasizes the
recognition of data patterns or data regularities in a given scenario. Pattern recognition
involves classification and cluster of patterns.
3. Features of Pattern Recognition:
Pattern recognition completely rely on data and derives any outcome or model from
data itself
Pattern recognition system should recognize a familiar pattern quickly and accurate
Recognize and classify unfamiliar objects very quickly
Accurately recognize shapes and objects from different angles
Identify patterns and objects even when partly hidden
Recognize patterns quickly with ease, and with automaticity
Pattern recognition always learn from data

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4. Training and Learning Model in Pattern Recognition:
Training and Learning is the building block model of Pattern Recognition. Learning is a
phenomenon through which a system gets trained and becomes adaptable to give
results in an accurate manner. Learning is the most important phase as to how well
the system performs on the data provided to the system depends on which
algorithms used on the data.
The model needs to undergo from two phases and the dataset is divided into two
categories, one which is used in training the model and called as the Training set and
the other is used in testing the model after training called as Testing set.

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4.1 Training set:
The training set is used to build a model. It consists of a set of images that are
used to train the system. Training rules and algorithms used to give relevant
information on how to associate input data with output decisions. The system is
trained by applying these algorithms on the dataset, all the relevant information
is extracted from the data and results are obtained. Generally, 80–85% of the
data of the dataset is taken for training data.
4.2 Testing set:
Testing data is used to test the system. It is the set of data that is used to verify
whether the system is producing the correct output after being trained or not.
Generally, 20% of the data of the dataset is used for testing. Testing data is used to
measure the accuracy of the system.

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5. Applications of Pattern recognition:
Computer vision: Pattern recognition is used to extract meaningful features from
given image/video samples and is used in computer vision for various applications
like biological and biomedical imaging.
Image processing, segmentation, and analysis: Pattern recognition is used to give
human recognition intelligence to a machine which is required in image processing.
Pattern recognition is used in Terrorist Detection Credit Fraud Detection Credit
Applications
Fingerprint identification: fingerprint recognition technology is a dominant
technology in the biometric market. A number of recognition methods have been
used to perform fingerprint matching out of which pattern recognition approaches
are widely used.

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Seismic analysis: The pattern recognition approach is used for the discovery,
imaging, and interpretation of temporal patterns in seismic array recordings.
Statistical pattern recognition is implemented and used in different types of seismic
analysis models.
Radar signal analysis: Pattern recognition and signal processing methods are used in
various applications of radar signal classifications like AP mine detection and
identification.
Speech recognition: The greatest success in speech recognition has been obtained
using pattern recognition paradigms. It is used in various algorithms of speech
recognition which tries to avoid the problems of using a phoneme level of description
and treats larger units such as words as a pattern.

12. 6. Advantages of Pattern Recognition:
Pattern recognition can interpret DNA Sequences
Pattern recognition has extensive application in astronomy, medicine, robotics,
and remote sensing by satellites
Pattern recognition solves classification problems
Pattern recognition solves the problem of fake biometric detection
It is useful for cloth pattern recognition for visually impaired blind people
We can recognize a particular object from a different angle
Pattern recognition helps in forensic Lab
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7. Difference Between Machine Learning and Pattern Recognition:

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