FAQ on Machine Learning

Instance based learning algorithm is also referred as Lazy learning algorithm as they delay the induction or generalization process until classification is performed. Instance-based learning algorithms are sometimes referred to as “lazy learning” algorithms because they don’t involve a traditional learning phase where the model is trained on the entire dataset to build a generalized … Read more

Bayesian Network is used to represent the graphical model for probability relationship among a set of variables. A Bayesian Network (BN) is a probabilistic graphical model that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG). In a Bayesian Network, nodes represent variables, and edges represent probabilistic dependencies between … Read more

Bayesian logic program consists of two components. The first component is a logical one ; it consists of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second component is a quantitative one, it encodes the quantitative information about the domain. In Bayesian logic programming, there are typically two main … Read more

In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs. The perceptron is a fundamental concept in machine learning, specifically in the realm of supervised learning and neural networks. It is one of the simplest types of artificial neural networks, proposed by Frank Rosenblatt in … Read more

The difference is that the heuristics for decision trees evaluate the average quality of a number of disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with the candidate rule. In the context of machine learning, heuristics for rule learning and heuristics for decision trees serve similar … Read more