PLAGIARISM FREE WRITING SERVICE
We accept
MONEY BACK GUARANTEE
100%
QUALITY

Pattern Recognition and Classification Theory

Assignment 2 of Pattern recognition should contain the classification theory. The topics should cover:

  1. Introduction to Pattern Recognition, including; a) The idea of pattern recognition and its applications. b) Basic steps of the pattern recognition task. c) Popular techniques used in these steps. d) Various application regions of pattern recognition research.
  2. Bayesian classification rule, prior, posterior, loss function, risk, and minimum error rate classification.
  3. Discriminant functions, Normal densities and application of Bayesian rule to normal densities with 3 different cases of variances and covariance matrices discussed in book.

As the name indicates, the pattern recognition is the classification of your pattern to 1 of the pre-specified classes. The procedure of understanding or recognizing the patterns by taking the raw data from a sensor, convert that raw data into some meta data by pre-processing that raw data, producing segments of the data through some kind of segmentation process and the pass those segment through some feature extractor which lead the purification of the raw data to be understood by the Classifier. Based on the feature extracted will classify it to a certain class which has already been defined by the decision boundary. The decision boundary is from some training data and the price related to it.

In short, the process of identifying object or pattern into some sort of classes predicated on some features which can be been described by the decision rule. A simple example for it the identification of the seabass and salmon fish passing through a conveyer belt. Certain features like the height, width and lightness can be used to create a decision boundary and put any fish into its respected class (sea bass or salmon).

It is the analysis of in what way the machines take notice of the environment, come to learn about the different patterns and make a rational decision about the class of the patterns.

A typical pattern recognition system contains the next components:

  1. Physical Environment
  2. Data Acquisition/Sensing
  3. Pre-Processing
  4. Feature Extraction
  5. Features
  6. Classification
  7. Post-Processing
  8. Decision Making

The previously listed components receive in the Figure 1.

Figure 1 Components of a Pattern Recognition System.

  1. How to overcome the insufficiency of vector space?
  2. Numerous amount of training data.
  3. Anonymous distributions of classes.
  4. Unidentified problem complexity.
  5. Generalization problems.
  6. Evaluation problems.

Given below are few of the pattern recognition potential research areas:

  • Adaptive signal processing
  • Machine learning
  • Artificial neural networks
  • Robotics and vision
  • Cognitive sciences
  • Mathematical statistics
  • Nonlinear optimization
  • Exploratory data analysis
  • Fuzzy and genetic systems
  • Detection and estimation theory
  • Formal languages
  • Structural modeling
  • Biological cybernetics
  • Computational neuroscience

Pattern recognition has outnumbered amount of applications, a few of which are as follows:

  • Image processing
  • Computer vision
  • Speech recognition
  • Multimodal interfaces
  • Automated target recognition
  • Optical character recognition
  • Seismic analysis
  • Man and machine diagnostics
  • Fingerprint identification
  • Industrial inspection
  • Financial forecast
  • Medical diagnosis
  • ECG signal analysis

Given here are the essential steps involved with pattern recognition:

Sensing: The pattern recognition systems need a sensor at the input to be able to adopt raw data from the surroundings in to the system.

Segmentation: It is done after the pre-processing step. In some systems this is actually the pre-processing step used for converting the raw data into some sorted data for the feature extraction.

Feature Extraction: Some specific parameters of the pattern are measured in this step like length in the fish example.

Classification: The patterns are then classified through some kind of classifiers like Bayesian Classifier. Classification is performed for putting the pattern into a specific class or category e. g. sea bass or salmon.

Post Processing: This step is done for even more improvement of the performance.

Figure 2 Steps involved with Pattern Recognition.

Classification techniques:

Bayes classifier, HMM, Kth Nearest Neighbor (KNN), Artificial Neural Network (ANN), Support Vector Machines (SVM), Training (parameter finding) & testing (decoding) etc.

Data representation techniques:

The compacting technique is utilized for enhancing the characteristics top features of data using various transformation methods like the Fourier Transform method, WT etc.

Dimensionality reduction:

Reduce the info dimensions by removing the mutually correlated features which results in the reduction of the normal information to make a set of nearly real informative parameters.

e. g. Principle Component Analysis, Linear Discriminant Analysis etc.

Transformations:

Various transformation techniques are also used like Fourier Transforms, Fast Fourier Transform etc.

The following are the potential research areas in the field of pattern recognition:

  • Adaptive signal processing
  • Machine learning
  • Artificial neural networks
  • Robotics and vision
  • Cognitive sciences
  • Mathematical statistics
  • Nonlinear optimization
  • Exploratory data analysis
  • Fuzzy and genetic systems
  • Detection and estimation theory
  • Formal languages
  • Structural modeling
  • Biological cybernetics
  • Computational neuroscience

The possibility of circumstances of nature that show how likely is the fact, that one state of nature would occur. For example, in the fish example it is considering that the last of the salmon is 0. 85. This imply that salmon is 85% more likely to seem than the ocean bass. If quantity of classes are c, then:

It is the likelihood of a specific state of nature given that observables have occurred. Mathematically,

Notice that,

It shows the cost related to each wrong action or decision we take. Mathematically,

The zero-one is the most commonly used loss function. It assigns zero on no loss in case there is correct decision while in case there is incorrect decision, it requires a uniform unit loss. Mathematically,

The expected loss is also called as conditional risk. It is defined as the summation of the product of loss occurred from each decision to its posterior probability. Mathematically:

Overall risk is distributed by:

From above equation we come to learn that by selecting only those action ± (. ) that minimize the for all those values of x will minimize the entire risk which is directly associated with the error thus minimize the error rate.

More than 7 000 students trust us to do their work
90% of customers place more than 5 orders with us
Special price $5 /page
PLACE AN ORDER
Check the price
for your assignment
FREE