In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of several possible non-binary outputs.
Genetic programming is one of the two techniques used in machine learning. The model is based on the testing and selecting the best choice among a set of results.
When there is sufficient data ‘Isotonic Regression' is used to prevent an overfitting issue.
To solve a particular computational program, multiple models such as classifiers or experts are strategically generated and combined. This process is known as ensemble learning.
In Machine Learning and statistics, dimension reduction is the process of reducing the number of random variables under considerations and can be divided into feature selection and feature extraction
The different approaches in Machine Learning are
☛ a) Concept Vs Classification Learning
☛ b) Symbolic Vs Statistical Learning
☛ c) Inductive Vs Analytical Learning
Ensemble learning is used to improve the classification, prediction, function approximation etc of a model.
The standard approach to supervised learning is to split the set of example into the training set and the test.
In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data. The main advantage is that it can't learn interactions between features.