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# Top 15 Machine Learning Algorithms That Have Been Forgotten | Technology Top 15 Machine Learning Algorithms That Have Been Forgotten

## Top 15 Forgotten Machine Learning Algorithms

One approach to non-linear dimensionality reduction, manifold learning algorithms, assumes that the dimensionality of a data set is only artificially high.

In 1967 the 'nearest neighbor algorithm', one of the first machine learning algorithms, was conceived, laying the foundation for basic pattern recognition. Today, in 2022, we have nothing less than great algorithms like Linear Regression, KNN, SVM, Decision Tree etc. Machine learning algorithms are integral to helping computer systems improve their performance and automatically build mathematical models using training data. These algorithms allow machines to make decisions without specific human instructions all the time. Over the years of computer science, algorithms have been developed and updated, and many of them have been lost in space. Analytics India Magazine will take you down this time machine rundown of some of the most popular yet forgotten machine learning algorithms.

## Survival Analysis

Survival Analysis, part of statistics, analyzes the expected duration of an event's occurrence. It can be applied to tasks such as period analysis or proportionality questions, answering what part of a population will survive at a given time or when the current population will become extinct, and more. In engineering, it is also known as reliability theory or reliability analysis as it helps to study the reliability of any system which is going to be shut down or destroyed one day.

## Evolutionary Algorithms

As a subset of evolutionary computation, evolutionary algorithms are general population-based metaheuristic optimization algorithms. They provide a heuristic-based approach to solving problems that cannot be easily solved in polynomial time. The approaches used are inspired by biological evolution and occur in four processes: initialization, selection, genetic operators, and termination.

## Hidden Markov Models (HMM)

The Hidden Markov Models (HMM) is a statistical model that describes the evolution of observable events. These events are not observable directly and depend on internal factors. A diverse class of probabilistic graphical models allow us to predict a sequence of unknown variables from a set of observed variables.

## Manifold Learn

One approach to non-linear dimensionality reduction, manifold learning algorithms, assumes that the dimensionality of a data set is only artificially high. The algorithm generalizes the linear structure to be sensitive to non-linear structures in the data. It is usually unusable because it learns the structure from the data itself.

Signal processing algorithms analyze, modify and synthesize sound signals, images and scientific measurements. These are typically built on three basic operations: add, multiply, and delay. Functions are combined to form complex algorithms in discrete-time systems. Traditional signal processing techniques include filtering, detection, estimation and frequency domain analysis.

Adaptive resonance theory is inspired by how the human brain processes information. The algorithm conceptualizes supervised and unsupervised learning methods for training neural networks, addressing pattern recognition, object recognition, and prediction.

## Self Organizing Maps (SOMs)

A self-organizing maps is an untrained neural network that is trained using untrained learning techniques to produce a low dimensional, discretized representation, known as a map, based on the input space of the training samples. Is. It is a way to reduce the data dimensions. They apply competing learning techniques to preserve all topological properties within the input space.

## Bayesian Non-Parametric Literature

Non-parametric statistics are usually distribution-free or have a specified distribution. The parameters of the distribution are unspecified and include descriptive statistics and statistical estimates.

## Edge Detection

Edge Detection Algorithms use mathematical methods to identify edges or curves in a digital image where there is an imbalance in image brightness. This algorithm is fundamental to image processing, machine vision and feature detection, and extraction in computer vision.

## Hierarchical Mixed-Effects Regression

Hierarchical modeling algorithms deal with data and their observations in a certain group. Hierarchical linear models or mixed-effects models are chosen approaches that allow researchers to account for ecological, contextual and individual-level or compositional variables.

Gradient Maps provide a basic view of spatial areas and their associated values ​​based on thematic issues. Ordinal maps follow the location map style, which refers to values ​​such as area, to visualize the data with spatial context.

## Symbolic Regression

One type of regression analysis, the symbolic regression algorithm, finds a suitable model based on a given dataset and mathematical expressions. These initial expressions are the outputs of random combinations of mathematical concepts such as mathematical operators, analytic functions, constants, and state variables.

## Conditional Random Field

A class of statistical modelling, conditional random field algorithms help in pattern recognition and structured prediction. The CRF models the predictors as a graphical model to represent the various dependencies between the predictors and to account for the different variables.

## Polynomial Regression

Polynomial Regression algorithm is a type of regression analysis. The regression algorithm models the relationship between a dependent (y) and an independent variable (x) as an 'nth' degree polynomial. Essentially, it provides the best estimate of the relationship between the dependent and independent variables.

## Multidimensional Scaling

Multidimensional Scaling algorithms are used to visualize the similarity level of individual cases of a dataset. This algorithm belongs to the branch of unsupervised ML and is a good technique for preserving global and local structures of high-dimensional data.