The Bayesian Classification represents a supervised learning method as well as a statistical method for classification. Assumes an underlying probabilistic model and it allows us to capture uncertainty about the model in a principled way by determining probabilities of the outcomes. It can solve diagnostic and predictive problems This Classification is named after Thomas Bayes … Read more
Nearest-Neighbor Classifiers requires three things The set of stored records Distance Metric to compute distance between records The value of k, the number of nearest neighbors to retrieve To classify an unknown record: Compute distance to other training records Identify k nearest neighbors Use class labels of nearest neighbors to determine the class label of … Read more
The objective of Linear Discriminant Analysis (LDA) is to perform dimensionality reduction while preserving as much of the class discriminatory information as possible Assume we have a set of D-dimensional samples {x(1, x(2, …, x(N}, N1 of which belong to class ω1, and N2 to class ω2. We seek to obtain a scalar y by … Read more
Principal components analysis (PCA) is one of a family of techniques for taking high-dimensional data, and using the dependencies between the variables to represent it in a more tractable, lower-dimensional form, without losing too much information. PCA is one of the simplest and most robust ways of doing such dimensionality reduction. It is also one … Read more
All key point of Statistical Pattern Recognition In statistical pattern recognition, we use vectors to represent patterns and class labels from a label set The abstractions typically deal with probability density/distributions of points in multi-dimensional spaces, trees and graphs, rules, and vectors themselves. Because of the vector space representation, it is meaningful to talk of … Read more
A pattern is either a physical object, for example a book or a chair or an abstract notion, like style of talking, or style of writing. It is also a shared property of a set of objects; for example, chairs, rectangles, or blue colored objects. Cognition is the act of seeing or perceiving, whereas recognition … Read more
Reinforcement learning Reinforcement learning is like many topics with names ending in -ing, such as machine learning, planning, and mountaineering, in that it is simultaneously a problem, a class of solution methods that work well on the class of problems, and the field that studies these problems and their solution methods. Reinforcement learning problems involve … Read more
The EM algorithm is used for obtaining maximum likelihood estimates of parameters when some of the data is missing. More generally, however, the EM algorithm can also be applied when there is latent, i.e. unobserved, data which was never intended to be observed in the first place. In that case, we simply assume that the … Read more
Given the intractable sample complexity for learning Bayesian classifiers, we must look for ways to reduce this complexity. The Naive Bayes classifier does this by making a conditional independence assumption that dramatically reduces the number of parameters to be estimated when modeling P(X|Y), from our original 2(2 −1) to just 2n Conditional Independence Definition: Given … Read more