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.
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)
Traditional Signal Processing
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 (ART)
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)
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 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.
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
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 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.