Pattern Recognition

Topics: Regression analysis, Linear regression, Naive Bayes classifier Pages: 22 (5886 words) Published: August 30, 2013
Chapter 1 Overview of Classification and Regression
1.1 Introduction to Pattern Recognition
Pattern Recognition is used to categorization of sensory inputs.We will gives features to Pattern recognition system and it gives the classification. Examples of Pattern Recognition Tasks:      Reading’ facial expressions Recognizing Speech Reading a Document Identifying a person by fingerprints Diagnosis from medical images

Pattern Recognition System works like this: Pattern Feature Extractor X Classifier

Class Lable

Feature extractor makes some measurements on the input pattern. Where,    X is called Feature Vector. Often, X ∈R n Classifier maps each feature vector to a class label. Features to be used are problem-specific.

Features depend on the problem. Measure ‘relevant’ quantities. Some techniques available to extract ‘more relevant’ quantities from the initial measurements. (e.g., PCA). After feature extraction each pattern is a vector. Classifier is a function to map such vectors into class labels. Many general techniques of classifier design are available. Need to test and validate the final system.

1.2 Classifier
Classification is nothing but classifying into different classes. We gives some notations: Feature Space, X – Set of all possible feature vectors. Classifier: a decision rule or a function, h : X → {1, . . . , M}. Often, X = R n Convenient to take M = 2. Then we take the labels as {0, 1} or {−1, 1}. Then, any binary valued function on X is a classifier. What h to choose? We want correct or optimal classifier. 1

 

How to design classifiers? How to judge performance?  How to provide performance guarantees? We first consider the 2-class problem. We can handle the M > 2 case if we know how to handle 2-class problem. Simplest alternative: design M 2-class classifiers. ‘One Vs Rest’ There are other possibilities: e.g., Tree structured classifiers. The 2-class problem is the basic problem. Designing Classifiers Need to decide how feature vector values determine the class.(How different marks reflect goodness of candidate) .In most applications, not possible to design classifier from ’physics of the problem’. The difficulties are     Lot of variability in patterns of a single class Variability in feature vector values Feature vectors of patterns from different classes can be arbitrarily close. Noise in measurements

Basically two sets we consider, Training set and Testing Set. Training set is used to train a classifier and Testing will done on the random inputs for testing the classification accuracy. Generation of training set – Take representative patterns of known category (data collection) and obtain the feature vectors. (Choice of feature measurements). Designing a classifier is a typical problem of learning from examples. (Also called learning with a teacher).

Fig 1 Learning System

Nature of feedback from teacher determines difficulty of the learning problem. On base of that learning is classified into 3 categories. Supervised Learning In this, every input pattern that is used to train the network is associated with an output pattern, which is the target or desired pattern. A teacher is assumed to be present during the learning process, when a comparison is made between the network’s computed output and the correct expected output, to determine an error. The error then can be used to change network parameters, which result in an improvement in performance. Unsupervised Learning In this learning method, the target output is not presented to the network. It is as if there is no teacher to present the desired patterns and hence, the system learns of its own by discovering and adapting the structural features in the input patterns. Reinforced Learning In this method, the teacher though available, does not present the expected answer but only indicates if the computed output is correct or incorrect. The information provided helps the network in its learning process. A...
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