Nearest-Neighbor Classifier in Artificial intelligence

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

Principal Components Analysis in Artificial intelligence

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

Design principles of pattern recognition system in Artificial intelligence

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

The EM Algorithm in Artificial intelligence

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

Statistical learning models in Artificial intelligence

To understand Statistical learning models in Artificial intelligence Consider a simple example. Our favorite Surprise candy comes in two flavors: cherry (yum) and lime (ugh). The manufacturer has a peculiar sense of humor and wraps each piece of candy in the same opaque wrapper, regardless of flavor. The candy is sold in very large bags, … Read more

Decision trees in Artificial intelligence

A decision tree reaches its decision by performing a sequence of tests. Each internal node in the tree corresponds to a test of the value of one of the input attributes, Ai, and the branches from the node are labeled with the possible values of the attribute, Ai = vik. Each leaf node in the … Read more

Resolution in Artificial intelligence

Resolution method is an inference rule which is used in both Propositional as well as First-order Predicate Logic This method is basically used for proving the satisfiability of a sentence In resolution method Resolution in Propositional Logic In propositional logic, resolution method is the only inference rule which gives a new clause when two or … Read more

Inference in First order logic in Artificial intelligence

There are two ideas behind Inference in First order logic in Artificial intelligence convert the KB to propositional logic and use propositional inference a shortcut that manipulates on first-order sentences directly (resolution, will not be introduced here) Universal Instantiation infer any sentence by substituting a ground term (a term without variables) for the variable Examples … Read more

Theory of first order logic in Artificial intelligence

Whereas propositional logic assumes the world contains facts First-order logic (like natural language) assumes the world contains Objects: people, houses, numbers, colors, baseball games, wars, … Relations: red, round, prime, brother of, bigger than, part of, comes between, … Functions: father of, best friend, one more than, plus, … Models for FOL: Example Example Five … Read more

Natural Language Possessing in AI

Natural Language Possessing we know that People communicate in many different ways: through speaking and listening, making gestures, using specialised hand signals (such as when driving or directing traffic), using sign languages for the deaf, or through various forms of text. By text we mean words that are written or printed on a flat surface … Read more