Computer

Decision Tree Learning is one of the most successful techniques for supervised classification learning. It is one of the most widely used and practical methods…

Perceptron is intended to be a machine, rather than a program, and while its first implementation was in software for the IBM 704, it is…

Random Forest can flexibly incorporate missing data in the predictor variables. When missing data are encountered for a particular observation during model building, the prediction…

Spiking Neural Networks is an attempt to emphasize the neurological aspects of artificial neural computation. It is to carry out neural computation. This requires that…

This article describe about Boltzmann Machine, which is a network of symmetrically connected, coupled stochastic binary units that make stochastic decisions about whether to be…

Hopfield Networks are constructed from artificial neurons. It consists of a set of interconnected neurons which update their activation values asynchronously. The activation values are…

Autoencoder neural network is an unsupervised learning algorithm that applies backpropagation, setting the target values to be equal to the inputs. Autoencoder tries to learn…

Backpropagation is a “Gradient Descent” method of training in that it uses gradient information to modify the network weights to decrease the value of the…

Computational Intelligence is an offshoot of artificial intelligence in which the emphasis is placed on heuristic algorithms such as fuzzy systems, neural networks and evolutionary…

This article describe about Automated Reasoning, which is the art and science of getting computers to apply logical reasoning to solve problems in computer programs.…

Adversarial Machine Learning is the study of effective machine learning techniques against an adversarial opponent. It aims to enable the safe adoption of machine learning…

Adaptive Control involves modifying the control law used by a controller to cope with the fact that the parameters of the system being controlled are…