What is meant by expectation-maximization?

In statistics, an expectation–maximization (EM) algorithm is an iterative method to find (local) maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables.

What is meant by perceptron?

A perceptron is a simple model of a biological neuron in an artificial neural network. The perceptron algorithm classifies patterns and groups by finding the linear separation between different objects and patterns that are received through numeric or visual input.

What is a perceptron in neural networks?

A Perceptron is a neural network unit that does certain computations to detect features or business intelligence in the input data. It is a function that maps its input “x,” which is multiplied by the learned weight coefficient, and generates an output value ”f(x).

What is the perceptron algorithm used for?

The Perceptron is a linear machine learning algorithm for binary classification tasks. It may be considered one of the first and one of the simplest types of artificial neural networks. It is definitely not “deep” learning but is an important building block.

Does perceptron require supervised learning?

Perceptron is a single layer neural network and a multi-layer perceptron is called Neural Networks. Perceptron is a linear classifier (binary). Also, it is used in supervised learning.

What is the difference between K-Means and expectation maximization?

Answer : Process of K-Means is something like assigning each observation to a cluster and process of EM(Expectation Maximization) is finding likelihood of an observation belonging to a cluster(probability). This is where both of these processes differ.

Is K-Means and expectation maximization?

Expectation Maximization works the same way as K-means except that the data is assigned to each cluster with the weights being soft probabilities instead of distances. The advantage is that the model becomes generative as we define the probability distribution for each model.