Genetic Algorithm
/ September 21, 2017

Genetic algorithms are a part of evolutionary computing, which is a rapidly growing area of artificial intelligence. Nature has always been a great source of inspiration to all mankind. Genetic Algorithms (GAs) are search based algorithms based on the concepts of natural selection and genetics. GAs is a subset of a much larger branch of computation known as Evolutionary Computation Genetic algorithms are inspired by Darwin’s theory about evolution. Simply said, solution to a problem solved by genetic algorithms is evolved. Genetic Algorithms are a family of computational models inspired by evolution These algorithms encode a potential solution to a specific problem on a simple chromosomelike data structure and apply recombination operators to these structures so as to preserve critical information Genetic algorithms are often viewed as function optimizers although the range of problems to which genetic algorithms have been applied is quite broad. Definition of Genetic algorithm Genetic Algorithms are heuristic search approaches that are applicable to a wide range of optimization problems. This flexibility makes them attractive for many optimization problems in practice. Evolution is the basis of Genetic Algorithms. The current variety and success of species is a good reason for believing in the power of evolution. Species are…

What is Bayesian Classifier and how Bayesian Classifier works ?
/ September 12, 2017

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
/ September 11, 2017

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…

Support Vector Machine (SVM)
/ September 1, 2017

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
/ August 31, 2017

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,…