Data Science: Supervised Learning (Definitions)

"A training paradigm where the neural network is presented with an input pattern and a desired output pattern. The desired output is compared with the neural network output, and the error information is used to adjust the connection weights." (Joseph P Bigus, "Data Mining with Neural Networks: Solving Business Problems from Application Development to Decision Support", 1996)

"Learning in which a system is trained by using a teacher to show the system the desired response to an input stimulus, usually in the form of a desired output." (Guido J Deboeck and Teuvo Kohonen, "Visual explorations in finance with self-organizing maps", 2000)

"learning with a teacher; learning scheme in which the average expected difference between wanted output for training samples, and the true output, respectively, is decreased." (Teuvo Kohonen, "Self-Organizing Maps" 3rd Ed., 2001)

"Supervised learning, or learning from examples, refers to systems that are trained instead of programmed with a set of examples, that is, a set of input-output pairs." (Tomaso Poggio & Steve Smale, "The Mathematics of Learning: Dealing with Data", Notices of the AMS, 2003)

"Methods, which use a response variable to guide the analysis." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A learning method in which there are two distinct phases to the operation. In the first phase each possible solution to a problem is assessed based on the input signal that is propagated through the system producing output respond. The actual respond produced is then compared with a desired response, generating error signals that are then used as a guide to solve the given problems using supervised learning algorithms". (Masoud Mohammadian, "Supervised Learning of Fuzzy Logic Systems", 2009)

"The set of learning algorithms in which the samples in the training dataset are all labelled." (Jun Jiang & Horace H S Ip, "Active Learning with SVM", Encyclopedia of Artificial Intelligence, 2009) 

"type of learning where the objective is to learn a function that associates a desired output (‘label’) to each input pattern. Supervised learning techniques require a training dataset of examples with their respective desired outputs. Supervised learning is traditionally divided into regression (the desired output is a continuous variable) and classification (the desired output is a class label)." (Óscar Pérez & Manuel Sánchez-Montañés, "Class Prediction in Test Sets with Shifted Distributions", 2009)

"Supervised learning is a type of machine learning that requires labeled training data." (Ivan Idris, "Python Data Analysis", 2014)

"Supervised learning refers to an approach that teaches the system to detect or match patterns in data based on examples it encounters during training with sample data." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"The knowledge is obtained through a training which includes a data set called the training sample which is structured according to the knowledge base supported by human experts as physicians in medical context, and databases. It is assumed that the user knows beforehand the classes and the instances of each class." (Nuno Pombo et al, "Machine Learning Approaches to Automated Medical Decision Support Systems", 2015)

"In supervised learning, a machine learning program is trained with sample items or documents that are labeled by category, and the program learns to assign new items to the correct categories." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

"A form of machine learning in which the goal is to learn a function that maps from a set of input attribute values for an instance to an estimate of the missing value for the target attribute of the same instance." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"A supervised learning algorithm applies a known set of input data and drives a model to produce reasonable predictions for responses to new data. Supervised learning develops predictive models using classification and regression techniques." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2018)

"It consists in learning from data with a known-in-advance outcome that is predicted based on a set of inputs, referred to as 'features'." (Iva Mihaylova, "Applications of Artificial Neural Networks in Economics and Finance", 2018)

"Supervised learning is the data mining task of inferring a function from labeled training data." (Dharmendra S Rajput et al, "Investigation on Deep Learning Approach for Big Data: Applications and Challenges", 2018)

"A particular form of learning process that takes place under supervision and that affects the training of an artificial neural networks." (Gaetano B Ronsivalle & Arianna Boldi, "Artificial Intelligence Applied: Six Actual Projects in Big Organizations", 2019)

"A type of machine learning in which output datasets train the machine to generate the desired algorithms, like a teacher supervising a student." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"In this learning, the model needs a labeled data for training. The model knows in advance the answer to the questions it must predict and tries to learn the relationship between input and output." (Aman Kamboj et al, "EarLocalizer: A Deep-Learning-Based Ear Localization Model for Side Face Images in the Wild", 2019)

"A machine learning task designed to learn a function that maps an input onto an output based on a set of training examples (training data). Each training example is a pair consisting of a vector of inputs and an output value. A supervised learning algorithm analyzes the training data and infers a mapping function. A simple example of supervised learning is a regression model." (Timofei Bogomolov et al, "Identifying Patterns in Fresh Produce Purchases: The Application of Machine Learning Techniques", 2020)

"Supervised algorithms mean that a system is developed or modeled on predetermined set of sample data." (Neha Garg & Kamlesh Sharma, "Machine Learning in Text Analysis", 2020)

"A machine learning technique that involves providing a machine with data that is labeled." (Sujata Ramnarayan, "Marketing and Artificial Intelligence: Personalization at Scale", 2021)

"It is machine learning algorithm in which the model learns from ample amount of available labeled data to predict the class of unseen instances." (Gunjan Ansari et al, "Natural Language Processing in Online Reviews", 2021)

"Supervised learning aims at developing a function for a set of labeled data and outputs." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"The supervised learning algorithms are trained with a complete set of data and thus, the supervised learning algorithms are used to predict/forecast." (M Govindarajan, "Big Data Mining Algorithms", 2021)

"Supervised Learning is a type of machine learning in which an algorithm takes a labelled data set (data that’s been organized and described), deduces key features characterizing each label, and learns to recognize them in new unseen data." (Accenture)