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About 10,000 papers for “Supervised learning”

Supervised Learning

Rituparna Sen, Sourish Das · Indian statistical institute series · 2023

Cited by 1

supervised learning

2024

Citation: 'supervised learning' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.11522 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

Cited by 0

Supervised Learning

Dalia Chakrabarty · 2025

This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. The book provides methods for secured mechanistic learning of the function that represents this relationship between the output and input variables, where said learning is undertaken within the remit of real-world information that can be messy in different ways. For example, the available data may be highly multivariate or be high-dimensional, reflecting the nature of the output variable that could be a vector, or matrix, or even higher in dimension, as is often the case in a real-world application. Additionally, the data is noisy, and often it is small to moderately large in size in multiple applications. Another difficulty that regularly arises is that the training dataset – comprising pairs of values of the input and output –is such, that the sought function cannot be captured by a parametric shape, but is instead underlined by an inhomogeneous correlation structure. These difficulties notwithstanding, we desire a streamlined methodology that allows the learning of the inter variable relationship  – to ultimately permit fast and reliable predictions of the output, at newly recorded values of the input. In fact, occasions arise when one seeks values of the input at which a new output value is recorded, and such a demand is also addressed in the book. The generic solution to the problem of secured supervised learning amidst real-world messiness, lies in treating the sought inter-variable relation as a (function-valued) random variable, which, being random, is ascribed a probability distribution. Then recalling that distributions on the space of functions are given by stochastic processes, the sought function is proposed to be a sample function of a stochastic process. This process is chosen as one that imposes minimal constraints on the sought function – identified as a Gaussian Process (GP) in the book. Thus, the sought function can be inferred upon, as long as the co-variance function of the underlying GP is learnt, given the available training set. The book presents probabilistic techniques to undertake said learning, within the challenges borne by the data, and illustrates such techniques on real data. Learning of a function is always followed by closed-form prediction of the mean and dispersion of the output variable that is realised at a test input.   To help with the background, the book includes reviews on stochastic processes and basic probability theory. This will render the first half of the book useful for students across disciplines, while the latter half will be appreciated by students of numerate subjects at the postgraduate level or higher, including students of computational sciences, statistics and mathematics.

Cited by 0

Supervised Learning

Laura Igual, Santi Seguí · Undergraduate topics in computer science · 2024

Cited by 9

Supervised Learning

Deepti Chopra, Roopal Khurana · BENTHAM SCIENCE PUBLISHERS eBooks · 2023

Supervised learning involves training using well “labelled” training data, and on the basis of the training data, machines are able to predict the output. The labelled data means that the correct output is attached along with the corresponding input data. In supervised learning, as the name suggests, the training data acts as the supervisor and provides training to the machine to predict the correct output. This chapter discusses Statistical Decision Theory, Gaussian & Normal Distribution, Conditionally Independent Binary Components, Learning Beliefs Network and Nearest-Neighbour Methods.<br>

Cited by 2

Supervised Learning

Laura Igual, Santi Seguí · Undergraduate topics in computer science · 2017

Cited by 2

Supervised learning

Sadegh Raeisi, Sedighe Raeisi · 2023

In this section, we will introduce supervised learning. This is one of the most widely used types of machine learning. We start with problem definitions and terminologies. We break the supervised learning task into its key ingredients and study them one by one. We will later extend this modular approach to understand neural networks.

Cited by 0

Supervised Learning

Joydeep Bhattacharjee · Apress eBooks · 2019

Machine learning is the science of getting computers to act without being specifically programmed. This is done by implementing special algorithms that have the ability to detect patterns in data. From a developer point of view, this means creating a system that has access to relevant data, is able to feed the data to machine learning algorithms, and is able to take the output and redirect it to downstream processes and tasks.

Cited by 2

Supervised learning

Dirk Valkenborg, Melvin Geubbelmans, Axel-Jan Rousseau et al. · American Journal of Orthodontics and Dentofacial Orthopedics · 2023

Cited by 22Open access

Supervised Learning

Pádraig Cunningham, Matthieu Cord, Sarah Jane Delany · Cognitive technologies · 2008

Cited by 340

Supervised Learning

Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari et al. · 2023

Cited by 0

Supervised Learning

Hao Helen Zhang · Wiley StatsRef: Statistics Reference Online · 2017

Abstract Supervised learning is a central topic in statistics and machine learning. Two major types of supervised learning tasks are regression and classification. There is a rich literature of methods, algorithms, and theory developed for both regression and classification, including classical results for low‐dimensional problems and modern techniques for high‐dimensional data analysis. This article provides a review on supervised learning in terms of its basic principles, decision theory, and methods and tools. Model selection is a critical issue in supervised learning, and recent results in variable selection are also discussed.

Cited by 2

Supervised Learning

2017

The problem of learning has two versions: supervised and unsupervised, that is with or without knowing the true class/parameter of the sample. This chapter addresses the first version. The purpose of learning (or training) is to use the measurement vectors of the samples to build a classifier or an estimator. The boosting algorithm offers an indirect way of converting a weak classifier to a strong one. The chapter discusses two approaches to supervised learning: parametric and non-parametric learning. The basic assumption in parametric learning is that the only unknown factors are parameters of the probability densities involved. Thus, learning from samples boils down to finding the suitable values of these parameters. Non-parametric methods are learning methods for which prior knowledge about the functional form of the conditional probability distributions is not available or is not used explicitly.

Cited by 1