What is Bayesian Classifier and how Bayesian Classifier works ?

Bayesian classifier: introduction A Bayesian classifier is based on idea that can predict values of features for members of learned classes. In data set patterns are grouped in classes because they have common features. Such classes are often called natural kinds. The idea behind a Bayesian classifier is that, if someone knows the classes, it can predict values of  similar other patterns. If it does not know the class, Bayes’ rule can be used to predict the class given according to attributes. In a Bayesian classifier, the learning model is a probabilistic model of attributes and that predict the classification labels of a new similar Pattern. A latent variable is a probabilistic variable that is not observed. A Bayesian classifier is a probabilistic model where the classification is a latent variable that is probabilistically related to the observed variables. Classification then becomes inference in the probabilistic model. Naive Bayes is a family of probabilistic algorithms that take advantage of probability theory and Bayes’ Theorem to predict the category of a sample (like a piece of news or a customer review). They are probabilistic, which means that they calculate the probability of each category for a given sample, and then output the category with the highest one. The…

Image Segmentation

Overview The division of an image into meaningful structures, image segmentation, is often an essential step in image analysis, object representation, visualization, and many other image processing tasks. Segmentation partitions an image into distinct regions containing each pixel with similar attributes. To be meaningful and useful for image analysis and interpretation, the regions should strongly relate to depicted objects or features of interest. Meaningful segmentation is the first step from low-level image processing transforming a greyscale or colour image into one or more other images to high-level image description in terms of features, objects, and scenes. The success of image analysis depends on reliability of segmentation, but an accurate partitioning of an image is generally a very challenging problem. Image segmentation is the division of an image into regions or categories, which correspond to different objects or parts of objects. Every pixel in an image is allocated to one of a number of these categories. A good segmentation is typically one in which: Pixels in the same category have similar greyscale of multivariate values and form a connected region, Neighboring pixels which are in different categories have dissimilar values The goal of image segmentation is to cluster pixels into salient…

An Overview of Particle swarm optimization (PSO)

Particle swarm optimization (PSO): introduction Particle swarm optimization (PSO) simulates the behaviors of bird flocking. Suppose the following scenario: a group of birds are randomly searching food in an area. There is only one piece of food in the area being searched. All the birds do not know where the food is. But they know how far the food is in each iteration. So what’s the best strategy to find the food? The effective one is to follow the bird which is nearest to the food. Particle swarm optimization (PSO) is a population based stochastic optimization technique developed by Dr. Eberhart and Dr. Kennedy in 1995. Definition Theory of particle swarm optimization (PSO) has been growing rapidly. PSO has been used by many applications of several problems. The algorithm of PSO emulates from behavior of animals societies that don’t have any leader in their group or swarm, such as bird flocking and fish schooling. Typically, a flock of animals that have no leaders will find food by random, follow one of the members of the group that has the closest position with a food source (potential solution). The flocks achieve their best condition simultaneously through communication among members who already have…

Support Vector Machine (SVM)

In recent years, the Artificial Neural Networks (ANNs) have been playing a significant role for variants of data mining tasks which is extensively popular and active research area among the researchers. To intend of neural network is to mimic the human ability to acclimatize to varying circumstances and the current environment. The subtle use of Support Vector Machine (SVM) in various data mining applications makes it an obligatory tool in the development of products that have implications for the human society. SVMs, being computationally powerful tools for supervised learning, are widely used in classification, clustering and regression problems. SVMs have been successfully applied to a variety of real-world problems like particle identification, face recognition, text categorization, bioinformatics, civil engineering and electrical engineering etc. SVM have attracted a great deal of attention in the last decade and actively applied to various domains applications. SVMs are typically used for learning classification, regression or ranking function. SVM are based on statistical learning theory and structural risk minimization principal and have the aim of determining the location of decision boundaries also known as hyperplane that produce the optimal separation of classes. Maximizing the margin and thereby creating the largest possible distance between the separating…

Neural Network

Neural Network Basics An Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. It is composed of a large number of highly interconnected processing elements (neurons) working in unison to solve specific problems. ANNs, like people, learn by example. An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurons. This is true of ANNs as well. Neural Network  Definition Work on artificial neural networks, commonly referred to as “neural networks,” has been motivated right from its inception by the recognition that the human brain computes in an entirely different way from the conventional digital computer. The brain is a highly complex, nonlinear, and parallel computer (information-processing system). It has the capability to organize its structural constituents, known as neurons, so as to perform certain computations (e.g., pattern recognition, perception, and motor control) many times faster than the fastest digital computer in existence today. Consider, for example,…

k Nearest Neighbor (KNN)

k Nearest Neighbor (KNN): introduction The necessity of data mining techniques has emerged quite immensely nowadays due to massive increase in data. Data mining is the process of extracting patterns and mining knowledge from data. K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970’s as a non-parametric technique. The model for KNN is the entire training dataset. When a prediction is required for a unseen data instance, the KNN algorithm will search through the training dataset for the k-most similar instances. The prediction attribute of the most similar instances is summarized and returned as the prediction for the unseen instance. Nearest neighbor classifiers is a lazy learner’s method and is based on learning by analogy. It is a supervised classification technique which is used widely. Unlike the previously described methods the nearest neighbor method waits until the last minute before doing any model construction on a given tuple. In this method the training tuples are represented in N-dimensional space. When given an unknown tuple, k-nearest neighbor classifier searches the k…

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