Pattern recognition
Introduction
Pattern recognition is not unfamiliar with everyone, it has a long history. PR is a subject researching object description and classification method, it is also a collection of mathe- matical, statistical, heuristic and inductive techniques of fundamental role in executing the tasks like human being on computers. In a sense, PR is figuring out actual problems via mathematical methods.
Pattern recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in some cases considered to be nearly synonymous with machine learning.[1] Pattern recognition systems are in many cases trained from labeled "training" data (supervised learning), but when no labeled data are available other algorithms can be used to discover previously unknown patterns (unsupervised learning).
What is pattern recognition?
When we decompose a complex problem we often find patterns among the smaller problems we create. The patterns are similarities or characteristics that some of the problems share.
Pattern recognition is one of the four cornerstones of Computer Science. It involves finding the similarities or patterns among small, decomposed problems that can help us solve more complex problems more efficiently.
Pattern recognition in Artificial Intelligence
A pattern is a short description of the data. Pattern recognition is how agents make predictions, which is a central problem in AI.
For example, suppose you observe a bit sequence like 01010101010101. If you can recognize the pattern of alternating zeros and ones, then you can predict that the sequence will continue that way.
Pattern Recognition and its Applications
Pattern recognition applications Pattern recognition is used in any area of science and engineering that studies the structure of observations. It is now frequently used in many applications in manufacturing industry, healthcare, and the military. Examples include the following. Optical character recognition (OCR) is becoming an integral part of document scanners, and is also used frequently in banking and postal applications. Printed characters can now be accurately recognized, and the improving performance of automatic recognition of handwritten cursive characters has diminished significantly the need of human interaction for OCR tasks.
• Scientific discipline dealing with methods for object description and classification
• Examples are – Agriculture : Crop Analysis, Soil Evaluation – Astronomy : Analysis of telescopic images – Biology :
Genetic studies – Engineering : Speech Recongition
• Iris Recognition
• Automated method of bio-metric identification that uses mathematical pattern-recognition techniques
• measure the unique patterns in the colored circle around your pupil to identify and authenticate
• Applications : physical access control in private enterprise and government,transportation, healthcare, and national identification programs • Examples of Iris Scanning – Iris scan, Project Iris.
• most accurate of the commonly used bio metric technologies
Pattern Recognition Methods
Pattern recognition undergoes an important developing for many years. Pattern recognition include a lot of methods which impelling the development of numerous applications in different filed. The practicability of these methods is intelligent emulation.
Statistical Pattern Recognition
Statistical decision and estimation theories have been commonly used in PR for a long time. It is a classical method of PR which was found out during a long developing process, it based on the feature vector distributing which getting from probability and statistical model. The statistical model is defined by a family of class-conditional
24 Vinita Dutt et al.: Pattern Recognition: an Overview
probability density functions Pr(x|Ci) (Probability of feature vector x given class Ci) In detail, in SPR, we put the features in some optional order, and then we can regard the set of features as a feature vector. Also statistical pat-tern recognition deals with features only without consider the relations between features.Data Clustering
Its aim is to find out a few similar clusters in a mass of data which not need any information of the known clusters. It is an unsupervised method. In general, the method of data clustering can be partitioned two classes, one is hierarchical clustering, and the other is partition clustering.
Syntactic Pattern Recognition
This method major emphasizes on the rules of composition. And the attractive aspect of syntactic methods is its suitability for dealing with recursion. When finish customizing a series of rules which can describe the relation among the parts of the object, syntactic pattern recognition which is a special kind of structural pattern recognition can be used.Pattern Recognition System
A pattern recognition system can be regarded as a process that allows it to cope with real and noisy data. Whether the decision made by the system is right or not mainly depending on the decision make by the human expert.
The need for Pattern Recognition in B2B Marketing
You might think that pattern recognition is an unusual subject for a B2B marketing, to be discussed in a blog post.
In the book Outliers, author Malcolm Gladwell says that it takes roughly ten thousand hours of practice to achieve mastery in a field. This is a statistic that is often quoted to show that true expertise usually doesn’t come cheaply. But that being said, there is wide variation in how long it takes individuals to master their craft, assuming that they ever do indeed achieve mastery.
For example, compare two individuals. The first person attends a prestigious culinary institute, followed by apprenticeship at a five-star restaurant. His or her goal is to learn from the best and get to the top of the profession as soon as possible. By contrast, individual two attended a couple of cooking classes at community college and went to work at a diner where he or she has been a short-order cook for the past several years. Both individuals have invested the same amount of time but the outcomes and skill levels are very different.
Every professional must go through a process of learning and experience to get to be recognized as an industry leader. And if they have completed the process effectively, they are worth more because they know what works and does not work in a particular scenario. For example, in the B2B marketing arena, my team and I have experienced lots of lead generation campaigns, lots of sales models, lots of branding exercises and lots of website optimization projects. We understand what it takes to drive awareness, leads and revenue.
This is what I mean by “pattern recognition.” It’s not just education or raw talent that counts, but the fact that you have been in the arena, made mistakes, taken the blows and delivered a few of your own. This real-world experience — assuming you learn from your mistakes – gives you the ability to recognize the pattern in a given scenario and make the right decision about whether and/or how to proceed. This ability to perform accurate pattern recognition doesn’t mean you will always get it right, but your chances of doing so are much greater. And employers or clients are almost always willing to pay for someone who can spot the patterns and guide decisions accordingly.
To sum up – to increase your value in B2B marketing (or any other endeavor), learn to master the art of pattern recognition.